Stable_baseline3_RL_algo_comparison.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"gpuType":"T4","authorship_tag":"ABX9TyNst1Q+7u6aeLM0l9Mi1VWs"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU"},"cells":[{"cell_type":"code","execution_count":19,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9_1KYXb7BUXY","executionInfo":{"status":"ok","timestamp":1702286350756,"user_tz":-60,"elapsed":20853,"user":{"displayName":"Alberto Saiz","userId":"03975085568355897980"}},"outputId":"2d23a424-faf0-4e8b-97c9-aa6137d3d5a7"},"outputs":[{"output_type":"stream","name":"stdout","text":["Requirement already satisfied: gymnasium in /usr/local/lib/python3.10/dist-packages (0.29.1)\n","Requirement already satisfied: numpy>=1.21.0 in /usr/local/lib/python3.10/dist-packages (from gymnasium) (1.23.5)\n","Requirement already satisfied: cloudpickle>=1.2.0 in /usr/local/lib/python3.10/dist-packages (from gymnasium) (2.2.1)\n","Requirement 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matplotlib->stable_baselines3) (3.1.1)\n","Requirement already satisfied: python-dateutil>=2.7 in /usr/local/lib/python3.10/dist-packages (from matplotlib->stable_baselines3) (2.8.2)\n","Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->stable_baselines3) (2023.3.post1)\n","Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.7->matplotlib->stable_baselines3) (1.16.0)\n","Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.10/dist-packages (from jinja2->torch>=1.13->stable_baselines3) (2.1.3)\n","Requirement already satisfied: mpmath>=0.19 in /usr/local/lib/python3.10/dist-packages (from sympy->torch>=1.13->stable_baselines3) (1.3.0)\n"]}],"source":["!pip install gymnasium\n","!pip install renderlab\n","!pip install mujoco\n","! pip install stable_baselines3"]},{"cell_type":"code","source":["import gymnasium as gym\n","import numpy as np\n","from stable_baselines3 import SAC, TD3, A2C, PPO\n","import numpy as np\n","import matplotlib.pyplot as plt\n","env = gym.make('Humanoid-v4', render_mode = \"rgb_array\")"],"metadata":{"id":"7eltHLk-Coxi","executionInfo":{"status":"ok","timestamp":1702286351208,"user_tz":-60,"elapsed":16,"user":{"displayName":"Alberto Saiz","userId":"03975085568355897980"}}},"execution_count":20,"outputs":[]},{"cell_type":"code","source":["past_max_torso_position = [-9999]\n","step=0\n","past_rewards = []\n","past_torso_positions = []"],"metadata":{"id":"1zG_sP7Oy0lt","executionInfo":{"status":"ok","timestamp":1702286351208,"user_tz":-60,"elapsed":15,"user":{"displayName":"Alberto Saiz","userId":"03975085568355897980"}}},"execution_count":21,"outputs":[]},{"cell_type":"code","source":["def jump_reward(reward):\n","    global step, past_rewards, past_max_torso_position\n","    z_torso=env.state_vector()[0] #position of torso\n","\n","    if z_torso < 0:\n","        salto = z_torso*5\n","    else:\n","        salto = z_torso*20\n","\n","    reward=reward + salto\n","    past_rewards.append(reward)\n","\n","    step += 1\n","    return reward"],"metadata":{"id":"ZAlQopsBCqmg","executionInfo":{"status":"ok","timestamp":1702286351208,"user_tz":-60,"elapsed":15,"user":{"displayName":"Alberto Saiz","userId":"03975085568355897980"}}},"execution_count":22,"outputs":[]},{"cell_type":"code","source":["from gym import RewardWrapper\n","env = gym.wrappers.TransformReward(env, jump_reward)\n","env.reset()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"2Q_JmkV7CuwF","executionInfo":{"status":"ok","timestamp":1702286351208,"user_tz":-60,"elapsed":14,"user":{"displayName":"Alberto Saiz","userId":"03975085568355897980"}},"outputId":"1fa67e35-4df0-4923-bb4a-6ac9caef376f"},"execution_count":23,"outputs":[{"output_type":"execute_result","data":{"text/plain":["(array([ 1.39620927e+00,  9.99970691e-01,  5.71564612e-03,  4.03970047e-03,\n","        -3.10296140e-03,  9.15286897e-03,  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     0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n","         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n","         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n","         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00,\n","         0.00000000e+00,  0.00000000e+00,  0.00000000e+00,  0.00000000e+00]),\n"," {})"]},"metadata":{},"execution_count":23}]},{"cell_type":"markdown","source":["# StableBaseline3 Models\n"],"metadata":{"id":"_X5KXis0DIu2"}},{"cell_type":"code","source":["model_train_SAC = SAC('MlpPolicy', env, verbose=1, device='cuda')\n","model_train_TD3 = TD3('MlpPolicy', env, verbose=1, device='cuda')\n","model__train_A2C = A2C('MlpPolicy', env, verbose=1, device='cuda')\n","model_train_PPO = PPO('MlpPolicy', env, verbose=1, device='cuda')"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"bcJ_3b0TDM5S","executionInfo":{"status":"ok","timestamp":1702286351209,"user_tz":-60,"elapsed":13,"user":{"displayName":"Alberto Saiz","userId":"03975085568355897980"}},"outputId":"ba4665eb-c74a-4e46-e174-e99579319259"},"execution_count":24,"outputs":[{"output_type":"stream","name":"stdout","text":["Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n"]}]},{"cell_type":"markdown","source":["# Comparison of models in test environment"],"metadata":{"id":"Vz1kDrwCE8AC"}},{"cell_type":"code","source":["def test_model(env, model, video=None, msg=None):\n","\n","    # Reset environment\n","    obs, info = env.reset()\n","    ep_len = 0\n","    ep_rew = 0\n","\n","    # Run episode until complete\n","    while True:\n","\n","        # Provide observation to policy to predict the next action\n","        action, _ = model.predict(obs)\n","\n","        # Perform action, update total reward\n","        obs, reward, terminated, truncated, info = env.step(action)\n","        ep_rew += reward\n","        # Increase step counter\n","        ep_len += 1\n","\n","        # Check to see if episode has ended\n","        if terminated or truncated:\n","            return ep_len, ep_rew"],"metadata":{"id":"LvwfM8N4O3Rh","executionInfo":{"status":"ok","timestamp":1702286351209,"user_tz":-60,"elapsed":9,"user":{"displayName":"Alberto Saiz","userId":"03975085568355897980"}}},"execution_count":25,"outputs":[]},{"cell_type":"code","source":["def learn_by_algs(alg):\n","      if alg == 'SAC':\n","          model = SAC('MlpPolicy', env, verbose=1, device='cuda')\n","      elif alg == 'TD3':\n","          model = TD3('MlpPolicy', env, verbose=1, device='cuda')\n","      elif alg == 'A2C':\n","          model = A2C('MlpPolicy', env, verbose=1, device='cuda')\n","      else:\n","          model=PPO('MlpPolicy', env, verbose=1, device='cuda')\n","\n","      model.learn(total_timesteps=NUM_TRAINING_STEPS_PER_ROUND)\n","\n","      # Test the model in several episodes\n","      tot_ep_len = 0\n","      tot_ep_rew = 0\n","      for ep in range(NUM_TESTS_PER_ROUND):\n","          ep_len, ep_rew = test_model(env, model)\n","          # Accumulate average length and reward\n","          tot_ep_len += ep_len\n","          tot_ep_rew += ep_rew\n","      return tot_ep_len, tot_ep_rew\n"],"metadata":{"id":"XhqBdHxuPX_p","executionInfo":{"status":"ok","timestamp":1702286351209,"user_tz":-60,"elapsed":9,"user":{"displayName":"Alberto Saiz","userId":"03975085568355897980"}}},"execution_count":26,"outputs":[]},{"cell_type":"code","source":["# Training and testing hyperparameters\n","NUM_TRAINING_STEPS_PER_ROUND = 500\n","NUM_ROUNDS=20\n","NUM_TESTS_PER_ROUND = 1000\n","# Train and test the model for a number of rounds\n","algorithms=[\"SAC\", \"TD3\", \"A2C\", \"PPO\"]\n","avg_ep_lens = np.zeros((4, NUM_ROUNDS))\n","avg_ep_rews = np.zeros((4, NUM_ROUNDS))\n","for rnd in range(NUM_ROUNDS):\n","    print(str(rnd/NUM_ROUNDS *100)+ \" %\")\n","    for i in range(4):\n","      # Train the model\n","      alg=algorithms[i]\n","      tot_ep_len, tot_ep_rew=learn_by_algs(alg)\n","\n","      # Record and dieplay average episode length and reward\n","      avg_ep_len =tot_ep_len/NUM_TESTS_PER_ROUND\n","      avg_ep_lens[i][rnd]=avg_ep_len\n","      avg_ep_rew = tot_ep_rew/NUM_TESTS_PER_ROUND\n","      avg_ep_rews[i][rnd]=avg_ep_rew"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"5xuB7TC1D_yz","executionInfo":{"status":"ok","timestamp":1702289185692,"user_tz":-60,"elapsed":2834492,"user":{"displayName":"Alberto Saiz","userId":"03975085568355897980"}},"outputId":"3f834dce-ccf4-4343-a211-d8f5e75389db"},"execution_count":27,"outputs":[{"output_type":"stream","name":"stdout","text":["0.0 %\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 27.2     |\n","|    ep_rew_mean     | 145      |\n","| time/              |          |\n","|    episodes        | 4        |\n","|    fps             | 215      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 109      |\n","| train/             |          |\n","|    actor_loss      | 14.9     |\n","|    critic_loss     | 626      |\n","|    ent_coef        | 1        |\n","|    ent_coef_loss   | 0.00804  |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 8        |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 27.5     |\n","|    ep_rew_mean     | 143      |\n","| time/              |          |\n","|    episodes        | 8        |\n","|    fps             | 79       |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 220      |\n","| train/             |          |\n","|    actor_loss      | -26.6    |\n","|    critic_loss     | 14       |\n","|    ent_coef        | 0.965    |\n","|    ent_coef_loss   | -1.01    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 119      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 25.6     |\n","|    ep_rew_mean     | 131      |\n","| time/              |          |\n","|    episodes        | 12       |\n","|    fps             | 65       |\n","|    time_elapsed    | 4        |\n","|    total_timesteps | 307      |\n","| train/             |          |\n","|    actor_loss      | -30.4    |\n","|    critic_loss     | 11.5     |\n","|    ent_coef        | 0.938    |\n","|    ent_coef_loss   | -1.79    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 206      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 24.5     |\n","|    ep_rew_mean     | 125      |\n","| time/              |          |\n","|    episodes        | 16       |\n","|    fps             | 58       |\n","|    time_elapsed    | 6        |\n","|    total_timesteps | 392      |\n","| train/             |          |\n","|    actor_loss      | -33.9    |\n","|    critic_loss     | 6.38     |\n","|    ent_coef        | 0.913    |\n","|    ent_coef_loss   | -2.55    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 291      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 23.8     |\n","|    ep_rew_mean     | 120      |\n","| time/              |          |\n","|    episodes        | 20       |\n","|    fps             | 54       |\n","|    time_elapsed    | 8        |\n","|    total_timesteps | 475      |\n","| train/             |          |\n","|    actor_loss      | -36.9    |\n","|    critic_loss     | 11.2     |\n","|    ent_coef        | 0.89     |\n","|    ent_coef_loss   | -3.27    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 374      |\n","---------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 22.8     |\n","|    ep_rew_mean     | 112      |\n","| time/              |          |\n","|    episodes        | 4        |\n","|    fps             | 1252     |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 91       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 21.2     |\n","|    ep_rew_mean     | 110      |\n","| time/              |          |\n","|    episodes        | 8        |\n","|    fps             | 301      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 170      |\n","| train/             |          |\n","|    actor_loss      | -4.07    |\n","|    critic_loss     | 13.3     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 62       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 19.8     |\n","|    ep_rew_mean     | 105      |\n","| time/              |          |\n","|    episodes        | 12       |\n","|    fps             | 222      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 238      |\n","| train/             |          |\n","|    actor_loss      | -5.93    |\n","|    critic_loss     | 1.16     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 130      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 19.1     |\n","|    ep_rew_mean     | 102      |\n","| time/              |          |\n","|    episodes        | 16       |\n","|    fps             | 195      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 306      |\n","| train/             |          |\n","|    actor_loss      | -7.19    |\n","|    critic_loss     | 0.513    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 198      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 18.7     |\n","|    ep_rew_mean     | 100      |\n","| time/              |          |\n","|    episodes        | 20       |\n","|    fps             | 180      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 374      |\n","| train/             |          |\n","|    actor_loss      | -8.36    |\n","|    critic_loss     | 0.361    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 266      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 18.4     |\n","|    ep_rew_mean     | 99       |\n","| time/              |          |\n","|    episodes        | 24       |\n","|    fps             | 172      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 442      |\n","| train/             |          |\n","|    actor_loss      | -9.25    |\n","|    critic_loss     | 0.519    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 334      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 18.2     |\n","|    ep_rew_mean     | 97.9     |\n","| time/              |          |\n","|    episodes        | 28       |\n","|    fps             | 166      |\n","|    time_elapsed    | 3        |\n","|    total_timesteps | 510      |\n","| train/             |          |\n","|    actor_loss      | -10      |\n","|    critic_loss     | 1.1      |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 402      |\n","---------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","------------------------------------\n","| rollout/              |          |\n","|    ep_len_mean        | 20.6     |\n","|    ep_rew_mean        | 100      |\n","| time/                 |          |\n","|    fps                | 357      |\n","|    iterations         | 100      |\n","|    time_elapsed       | 1        |\n","|    total_timesteps    | 500      |\n","| train/                |          |\n","|    entropy_loss       | -24.1    |\n","|    explained_variance | -0.0464  |\n","|    learning_rate      | 0.0007   |\n","|    n_updates          | 99       |\n","|    policy_loss        | 82.9     |\n","|    std                | 0.998    |\n","|    value_loss         | 52.9     |\n","------------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 24.1     |\n","|    ep_rew_mean     | 122      |\n","| time/              |          |\n","|    fps             | 440      |\n","|    iterations      | 1        |\n","|    time_elapsed    | 4        |\n","|    total_timesteps | 2048     |\n","---------------------------------\n","5.0 %\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 29       |\n","|    ep_rew_mean     | 145      |\n","| time/              |          |\n","|    episodes        | 4        |\n","|    fps             | 389      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 116      |\n","| train/             |          |\n","|    actor_loss      | 4.28     |\n","|    critic_loss     | 374      |\n","|    ent_coef        | 0.998    |\n","|    ent_coef_loss   | -0.0321  |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 15       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 25.6     |\n","|    ep_rew_mean     | 129      |\n","| time/              |          |\n","|    episodes        | 8        |\n","|    fps             | 136      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 205      |\n","| train/             |          |\n","|    actor_loss      | -27.2    |\n","|    critic_loss     | 33.8     |\n","|    ent_coef        | 0.969    |\n","|    ent_coef_loss   | -0.878   |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 104      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 23.8     |\n","|    ep_rew_mean     | 119      |\n","| time/              |          |\n","|    episodes        | 12       |\n","|    fps             | 106      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 285      |\n","| train/             |          |\n","|    actor_loss      | -31      |\n","|    critic_loss     | 7.74     |\n","|    ent_coef        | 0.945    |\n","|    ent_coef_loss   | -1.6     |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 184      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 24.1     |\n","|    ep_rew_mean     | 122      |\n","| time/              |          |\n","|    episodes        | 16       |\n","|    fps             | 87       |\n","|    time_elapsed    | 4        |\n","|    total_timesteps | 386      |\n","| train/             |          |\n","|    actor_loss      | -35.1    |\n","|    critic_loss     | 7.22     |\n","|    ent_coef        | 0.915    |\n","|    ent_coef_loss   | -2.49    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 285      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 23.6     |\n","|    ep_rew_mean     | 119      |\n","| time/              |          |\n","|    episodes        | 20       |\n","|    fps             | 80       |\n","|    time_elapsed    | 5        |\n","|    total_timesteps | 473      |\n","| train/             |          |\n","|    actor_loss      | -38.8    |\n","|    critic_loss     | 6.74     |\n","|    ent_coef        | 0.891    |\n","|    ent_coef_loss   | -3.26    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 372      |\n","---------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 22       |\n","|    ep_rew_mean     | 112      |\n","| time/              |          |\n","|    episodes        | 4        |\n","|    fps             | 711      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 88       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 18.9     |\n","|    ep_rew_mean     | 93.3     |\n","| time/              |          |\n","|    episodes        | 8        |\n","|    fps             | 251      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 151      |\n","| train/             |          |\n","|    actor_loss      | -4.62    |\n","|    critic_loss     | 3.66     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 48       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 21.6     |\n","|    ep_rew_mean     | 120      |\n","| time/              |          |\n","|    episodes        | 12       |\n","|    fps             | 168      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 259      |\n","| train/             |          |\n","|    actor_loss      | -7.14    |\n","|    critic_loss     | 0.726    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 145      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 20.8     |\n","|    ep_rew_mean     | 115      |\n","| time/              |          |\n","|    episodes        | 16       |\n","|    fps             | 159      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 332      |\n","| train/             |          |\n","|    actor_loss      | -8.75    |\n","|    critic_loss     | 7.36     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 225      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 20.3     |\n","|    ep_rew_mean     | 113      |\n","| time/              |          |\n","|    episodes        | 20       |\n","|    fps             | 155      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 406      |\n","| train/             |          |\n","|    actor_loss      | -9.93    |\n","|    critic_loss     | 1.78     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 299      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 20.1     |\n","|    ep_rew_mean     | 112      |\n","| time/              |          |\n","|    episodes        | 24       |\n","|    fps             | 152      |\n","|    time_elapsed    | 3        |\n","|    total_timesteps | 483      |\n","| train/             |          |\n","|    actor_loss      | -10.8    |\n","|    critic_loss     | 2.22     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 376      |\n","---------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","------------------------------------\n","| rollout/              |          |\n","|    ep_len_mean        | 21.1     |\n","|    ep_rew_mean        | 103      |\n","| time/                 |          |\n","|    fps                | 363      |\n","|    iterations         | 100      |\n","|    time_elapsed       | 1        |\n","|    total_timesteps    | 500      |\n","| train/                |          |\n","|    entropy_loss       | -23.9    |\n","|    explained_variance | 0.478    |\n","|    learning_rate      | 0.0007   |\n","|    n_updates          | 99       |\n","|    policy_loss        | 322      |\n","|    std                | 0.986    |\n","|    value_loss         | 214      |\n","------------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 20.8     |\n","|    ep_rew_mean     | 100      |\n","| time/              |          |\n","|    fps             | 432      |\n","|    iterations      | 1        |\n","|    time_elapsed    | 4        |\n","|    total_timesteps | 2048     |\n","---------------------------------\n","10.0 %\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 24.5     |\n","|    ep_rew_mean     | 129      |\n","| time/              |          |\n","|    episodes        | 4        |\n","|    fps             | 1051     |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 98       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 23       |\n","|    ep_rew_mean     | 118      |\n","| time/              |          |\n","|    episodes        | 8        |\n","|    fps             | 157      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 184      |\n","| train/             |          |\n","|    actor_loss      | -26.8    |\n","|    critic_loss     | 15.2     |\n","|    ent_coef        | 0.974    |\n","|    ent_coef_loss   | -0.72    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 83       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 25.8     |\n","|    ep_rew_mean     | 137      |\n","| time/              |          |\n","|    episodes        | 12       |\n","|    fps             | 103      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 309      |\n","| train/             |          |\n","|    actor_loss      | -32      |\n","|    critic_loss     | 9.17     |\n","|    ent_coef        | 0.937    |\n","|    ent_coef_loss   | -1.83    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 208      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 24.2     |\n","|    ep_rew_mean     | 126      |\n","| time/              |          |\n","|    episodes        | 16       |\n","|    fps             | 87       |\n","|    time_elapsed    | 4        |\n","|    total_timesteps | 388      |\n","| train/             |          |\n","|    actor_loss      | -36      |\n","|    critic_loss     | 6.21     |\n","|    ent_coef        | 0.914    |\n","|    ent_coef_loss   | -2.54    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 287      |\n","---------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 23       |\n","|    ep_rew_mean     | 113      |\n","| time/              |          |\n","|    episodes        | 4        |\n","|    fps             | 1327     |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 92       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 22.2     |\n","|    ep_rew_mean     | 114      |\n","| time/              |          |\n","|    episodes        | 8        |\n","|    fps             | 296      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 178      |\n","| train/             |          |\n","|    actor_loss      | -5.56    |\n","|    critic_loss     | 1.71     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 72       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 19.5     |\n","|    ep_rew_mean     | 97.8     |\n","| time/              |          |\n","|    episodes        | 12       |\n","|    fps             | 233      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 234      |\n","| train/             |          |\n","|    actor_loss      | -6.62    |\n","|    critic_loss     | 0.514    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 128      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 18.1     |\n","|    ep_rew_mean     | 89.7     |\n","| time/              |          |\n","|    episodes        | 16       |\n","|    fps             | 206      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 290      |\n","| train/             |          |\n","|    actor_loss      | -7.37    |\n","|    critic_loss     | 0.191    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 184      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 17.3     |\n","|    ep_rew_mean     | 84.9     |\n","| time/              |          |\n","|    episodes        | 20       |\n","|    fps             | 191      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 346      |\n","| train/             |          |\n","|    actor_loss      | -8.04    |\n","|    critic_loss     | 0.538    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 240      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 16.6     |\n","|    ep_rew_mean     | 80.8     |\n","| time/              |          |\n","|    episodes        | 24       |\n","|    fps             | 181      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 398      |\n","| train/             |          |\n","|    actor_loss      | -8.76    |\n","|    critic_loss     | 0.218    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 293      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 16.1     |\n","|    ep_rew_mean     | 78.1     |\n","| time/              |          |\n","|    episodes        | 28       |\n","|    fps             | 167      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 451      |\n","| train/             |          |\n","|    actor_loss      | -9.4     |\n","|    critic_loss     | 0.43     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 346      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 15.7     |\n","|    ep_rew_mean     | 75.9     |\n","| time/              |          |\n","|    episodes        | 32       |\n","|    fps             | 157      |\n","|    time_elapsed    | 3        |\n","|    total_timesteps | 503      |\n","| train/             |          |\n","|    actor_loss      | -9.88    |\n","|    critic_loss     | 0.759    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 398      |\n","---------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","------------------------------------\n","| rollout/              |          |\n","|    ep_len_mean        | 20.4     |\n","|    ep_rew_mean        | 101      |\n","| time/                 |          |\n","|    fps                | 360      |\n","|    iterations         | 100      |\n","|    time_elapsed       | 1        |\n","|    total_timesteps    | 500      |\n","| train/                |          |\n","|    entropy_loss       | -23.9    |\n","|    explained_variance | 0.434    |\n","|    learning_rate      | 0.0007   |\n","|    n_updates          | 99       |\n","|    policy_loss        | 347      |\n","|    std                | 0.989    |\n","|    value_loss         | 258      |\n","------------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 21.6     |\n","|    ep_rew_mean     | 104      |\n","| time/              |          |\n","|    fps             | 511      |\n","|    iterations      | 1        |\n","|    time_elapsed    | 4        |\n","|    total_timesteps | 2048     |\n","---------------------------------\n","15.0 %\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 22       |\n","|    ep_rew_mean     | 112      |\n","| time/              |          |\n","|    episodes        | 4        |\n","|    fps             | 1127     |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 88       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 23.4     |\n","|    ep_rew_mean     | 117      |\n","| time/              |          |\n","|    episodes        | 8        |\n","|    fps             | 149      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 187      |\n","| train/             |          |\n","|    actor_loss      | -27.2    |\n","|    critic_loss     | 19.3     |\n","|    ent_coef        | 0.973    |\n","|    ent_coef_loss   | -0.749   |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 86       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 26.7     |\n","|    ep_rew_mean     | 142      |\n","| time/              |          |\n","|    episodes        | 12       |\n","|    fps             | 104      |\n","|    time_elapsed    | 3        |\n","|    total_timesteps | 320      |\n","| train/             |          |\n","|    actor_loss      | -34.1    |\n","|    critic_loss     | 20.2     |\n","|    ent_coef        | 0.934    |\n","|    ent_coef_loss   | -1.94    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 219      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 25       |\n","|    ep_rew_mean     | 131      |\n","| time/              |          |\n","|    episodes        | 16       |\n","|    fps             | 96       |\n","|    time_elapsed    | 4        |\n","|    total_timesteps | 400      |\n","| train/             |          |\n","|    actor_loss      | -36.8    |\n","|    critic_loss     | 8.79     |\n","|    ent_coef        | 0.911    |\n","|    ent_coef_loss   | -2.63    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 299      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 23.9     |\n","|    ep_rew_mean     | 123      |\n","| time/              |          |\n","|    episodes        | 20       |\n","|    fps             | 90       |\n","|    time_elapsed    | 5        |\n","|    total_timesteps | 478      |\n","| train/             |          |\n","|    actor_loss      | -40.7    |\n","|    critic_loss     | 8.42     |\n","|    ent_coef        | 0.889    |\n","|    ent_coef_loss   | -3.28    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 377      |\n","---------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 23.5     |\n","|    ep_rew_mean     | 115      |\n","| time/              |          |\n","|    episodes        | 4        |\n","|    fps             | 1370     |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 94       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 20.1     |\n","|    ep_rew_mean     | 100      |\n","| time/              |          |\n","|    episodes        | 8        |\n","|    fps             | 331      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 161      |\n","| train/             |          |\n","|    actor_loss      | -4.79    |\n","|    critic_loss     | 2.27     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 54       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 17.9     |\n","|    ep_rew_mean     | 89.2     |\n","| time/              |          |\n","|    episodes        | 12       |\n","|    fps             | 246      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 215      |\n","| train/             |          |\n","|    actor_loss      | -6.46    |\n","|    critic_loss     | 4.41     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 107      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 16.8     |\n","|    ep_rew_mean     | 82.5     |\n","| time/              |          |\n","|    episodes        | 16       |\n","|    fps             | 213      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 268      |\n","| train/             |          |\n","|    actor_loss      | -7.27    |\n","|    critic_loss     | 3.63     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 161      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 16       |\n","|    ep_rew_mean     | 78.1     |\n","| time/              |          |\n","|    episodes        | 20       |\n","|    fps             | 197      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 320      |\n","| train/             |          |\n","|    actor_loss      | -8.53    |\n","|    critic_loss     | 3.25     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 213      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 15.5     |\n","|    ep_rew_mean     | 75.2     |\n","| time/              |          |\n","|    episodes        | 24       |\n","|    fps             | 186      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 372      |\n","| train/             |          |\n","|    actor_loss      | -9.13    |\n","|    critic_loss     | 1.11     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 265      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 15.1     |\n","|    ep_rew_mean     | 73.2     |\n","| time/              |          |\n","|    episodes        | 28       |\n","|    fps             | 179      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 424      |\n","| train/             |          |\n","|    actor_loss      | -9.6     |\n","|    critic_loss     | 0.919    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 317      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 14.9     |\n","|    ep_rew_mean     | 71.7     |\n","| time/              |          |\n","|    episodes        | 32       |\n","|    fps             | 173      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 476      |\n","| train/             |          |\n","|    actor_loss      | -10.3    |\n","|    critic_loss     | 1.08     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 369      |\n","---------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","------------------------------------\n","| rollout/              |          |\n","|    ep_len_mean        | 19.8     |\n","|    ep_rew_mean        | 96       |\n","| time/                 |          |\n","|    fps                | 380      |\n","|    iterations         | 100      |\n","|    time_elapsed       | 1        |\n","|    total_timesteps    | 500      |\n","| train/                |          |\n","|    entropy_loss       | -23.9    |\n","|    explained_variance | -0.0351  |\n","|    learning_rate      | 0.0007   |\n","|    n_updates          | 99       |\n","|    policy_loss        | 144      |\n","|    std                | 0.987    |\n","|    value_loss         | 78.1     |\n","------------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 22.1     |\n","|    ep_rew_mean     | 107      |\n","| time/              |          |\n","|    fps             | 484      |\n","|    iterations      | 1        |\n","|    time_elapsed    | 4        |\n","|    total_timesteps | 2048     |\n","---------------------------------\n","20.0 %\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 24       |\n","|    ep_rew_mean     | 120      |\n","| time/              |          |\n","|    episodes        | 4        |\n","|    fps             | 1202     |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 96       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 22.5     |\n","|    ep_rew_mean     | 111      |\n","| time/              |          |\n","|    episodes        | 8        |\n","|    fps             | 151      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 180      |\n","| train/             |          |\n","|    actor_loss      | -23.8    |\n","|    critic_loss     | 22.9     |\n","|    ent_coef        | 0.976    |\n","|    ent_coef_loss   | -0.664   |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 79       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 23.4     |\n","|    ep_rew_mean     | 117      |\n","| time/              |          |\n","|    episodes        | 12       |\n","|    fps             | 97       |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 281      |\n","| train/             |          |\n","|    actor_loss      | -29      |\n","|    critic_loss     | 9.07     |\n","|    ent_coef        | 0.945    |\n","|    ent_coef_loss   | -1.59    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 180      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 25       |\n","|    ep_rew_mean     | 126      |\n","| time/              |          |\n","|    episodes        | 16       |\n","|    fps             | 83       |\n","|    time_elapsed    | 4        |\n","|    total_timesteps | 400      |\n","| train/             |          |\n","|    actor_loss      | -33.5    |\n","|    critic_loss     | 7.84     |\n","|    ent_coef        | 0.91     |\n","|    ent_coef_loss   | -2.64    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 299      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 24.1     |\n","|    ep_rew_mean     | 121      |\n","| time/              |          |\n","|    episodes        | 20       |\n","|    fps             | 82       |\n","|    time_elapsed    | 5        |\n","|    total_timesteps | 481      |\n","| train/             |          |\n","|    actor_loss      | -37.2    |\n","|    critic_loss     | 6.96     |\n","|    ent_coef        | 0.888    |\n","|    ent_coef_loss   | -3.36    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 380      |\n","---------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 21.5     |\n","|    ep_rew_mean     | 109      |\n","| time/              |          |\n","|    episodes        | 4        |\n","|    fps             | 1318     |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 86       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 18.8     |\n","|    ep_rew_mean     | 95.1     |\n","| time/              |          |\n","|    episodes        | 8        |\n","|    fps             | 355      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 150      |\n","| train/             |          |\n","|    actor_loss      | -5.98    |\n","|    critic_loss     | 6.98     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 49       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 17.5     |\n","|    ep_rew_mean     | 88.4     |\n","| time/              |          |\n","|    episodes        | 12       |\n","|    fps             | 243      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 210      |\n","| train/             |          |\n","|    actor_loss      | -6.69    |\n","|    critic_loss     | 0.265    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 111      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 16.4     |\n","|    ep_rew_mean     | 85.5     |\n","| time/              |          |\n","|    episodes        | 16       |\n","|    fps             | 213      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 262      |\n","| train/             |          |\n","|    actor_loss      | -7.6     |\n","|    critic_loss     | 0.216    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 163      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 15.7     |\n","|    ep_rew_mean     | 84       |\n","| time/              |          |\n","|    episodes        | 20       |\n","|    fps             | 197      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 314      |\n","| train/             |          |\n","|    actor_loss      | -8.33    |\n","|    critic_loss     | 0.21     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 215      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 15.2     |\n","|    ep_rew_mean     | 83       |\n","| time/              |          |\n","|    episodes        | 24       |\n","|    fps             | 186      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 366      |\n","| train/             |          |\n","|    actor_loss      | -8.98    |\n","|    critic_loss     | 0.256    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 267      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 14.9     |\n","|    ep_rew_mean     | 81.9     |\n","| time/              |          |\n","|    episodes        | 28       |\n","|    fps             | 177      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 418      |\n","| train/             |          |\n","|    actor_loss      | -9.64    |\n","|    critic_loss     | 0.399    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 319      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 14.7     |\n","|    ep_rew_mean     | 81.2     |\n","| time/              |          |\n","|    episodes        | 32       |\n","|    fps             | 171      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 470      |\n","| train/             |          |\n","|    actor_loss      | -10.2    |\n","|    critic_loss     | 0.507    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 371      |\n","---------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","------------------------------------\n","| rollout/              |          |\n","|    ep_len_mean        | 21.9     |\n","|    ep_rew_mean        | 109      |\n","| time/                 |          |\n","|    fps                | 372      |\n","|    iterations         | 100      |\n","|    time_elapsed       | 1        |\n","|    total_timesteps    | 500      |\n","| train/                |          |\n","|    entropy_loss       | -24      |\n","|    explained_variance | 0.0286   |\n","|    learning_rate      | 0.0007   |\n","|    n_updates          | 99       |\n","|    policy_loss        | 329      |\n","|    std                | 0.993    |\n","|    value_loss         | 219      |\n","------------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 21.8     |\n","|    ep_rew_mean     | 108      |\n","| time/              |          |\n","|    fps             | 546      |\n","|    iterations      | 1        |\n","|    time_elapsed    | 3        |\n","|    total_timesteps | 2048     |\n","---------------------------------\n","25.0 %\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 29.5     |\n","|    ep_rew_mean     | 150      |\n","| time/              |          |\n","|    episodes        | 4        |\n","|    fps             | 372      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 118      |\n","| train/             |          |\n","|    actor_loss      | -4.47    |\n","|    critic_loss     | 240      |\n","|    ent_coef        | 0.997    |\n","|    ent_coef_loss   | -0.0619  |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 17       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 27.1     |\n","|    ep_rew_mean     | 137      |\n","| time/              |          |\n","|    episodes        | 8        |\n","|    fps             | 136      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 217      |\n","| train/             |          |\n","|    actor_loss      | -27.4    |\n","|    critic_loss     | 18.2     |\n","|    ent_coef        | 0.965    |\n","|    ent_coef_loss   | -1       |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 116      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 25.2     |\n","|    ep_rew_mean     | 127      |\n","| time/              |          |\n","|    episodes        | 12       |\n","|    fps             | 112      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 303      |\n","| train/             |          |\n","|    actor_loss      | -31.7    |\n","|    critic_loss     | 11.4     |\n","|    ent_coef        | 0.939    |\n","|    ent_coef_loss   | -1.78    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 202      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 23.7     |\n","|    ep_rew_mean     | 118      |\n","| time/              |          |\n","|    episodes        | 16       |\n","|    fps             | 103      |\n","|    time_elapsed    | 3        |\n","|    total_timesteps | 379      |\n","| train/             |          |\n","|    actor_loss      | -34.8    |\n","|    critic_loss     | 10.2     |\n","|    ent_coef        | 0.917    |\n","|    ent_coef_loss   | -2.45    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 278      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 23.6     |\n","|    ep_rew_mean     | 119      |\n","| time/              |          |\n","|    episodes        | 20       |\n","|    fps             | 97       |\n","|    time_elapsed    | 4        |\n","|    total_timesteps | 473      |\n","| train/             |          |\n","|    actor_loss      | -38.9    |\n","|    critic_loss     | 10.3     |\n","|    ent_coef        | 0.89     |\n","|    ent_coef_loss   | -3.27    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 372      |\n","---------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 24.5     |\n","|    ep_rew_mean     | 122      |\n","| time/              |          |\n","|    episodes        | 4        |\n","|    fps             | 1341     |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 98       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 19.1     |\n","|    ep_rew_mean     | 93.6     |\n","| time/              |          |\n","|    episodes        | 8        |\n","|    fps             | 414      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 153      |\n","| train/             |          |\n","|    actor_loss      | -5.46    |\n","|    critic_loss     | 3.48     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 41       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 17.4     |\n","|    ep_rew_mean     | 84.2     |\n","| time/              |          |\n","|    episodes        | 12       |\n","|    fps             | 271      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 209      |\n","| train/             |          |\n","|    actor_loss      | -6.66    |\n","|    critic_loss     | 0.985    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 99       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 16.4     |\n","|    ep_rew_mean     | 78.4     |\n","| time/              |          |\n","|    episodes        | 16       |\n","|    fps             | 235      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 262      |\n","| train/             |          |\n","|    actor_loss      | -7.34    |\n","|    critic_loss     | 2.96     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 149      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 16       |\n","|    ep_rew_mean     | 76.3     |\n","| time/              |          |\n","|    episodes        | 20       |\n","|    fps             | 212      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 320      |\n","| train/             |          |\n","|    actor_loss      | -8.02    |\n","|    critic_loss     | 1.66     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 206      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 15.8     |\n","|    ep_rew_mean     | 75.2     |\n","| time/              |          |\n","|    episodes        | 24       |\n","|    fps             | 197      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 380      |\n","| train/             |          |\n","|    actor_loss      | -8.67    |\n","|    critic_loss     | 1.58     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 266      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 15.6     |\n","|    ep_rew_mean     | 73.9     |\n","| time/              |          |\n","|    episodes        | 28       |\n","|    fps             | 187      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 436      |\n","| train/             |          |\n","|    actor_loss      | -9.33    |\n","|    critic_loss     | 1.15     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 324      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 15.5     |\n","|    ep_rew_mean     | 73.2     |\n","| time/              |          |\n","|    episodes        | 32       |\n","|    fps             | 181      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 495      |\n","| train/             |          |\n","|    actor_loss      | -10      |\n","|    critic_loss     | 0.832    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 383      |\n","---------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","------------------------------------\n","| rollout/              |          |\n","|    ep_len_mean        | 21.6     |\n","|    ep_rew_mean        | 105      |\n","| time/                 |          |\n","|    fps                | 368      |\n","|    iterations         | 100      |\n","|    time_elapsed       | 1        |\n","|    total_timesteps    | 500      |\n","| train/                |          |\n","|    entropy_loss       | -24      |\n","|    explained_variance | -0.0901  |\n","|    learning_rate      | 0.0007   |\n","|    n_updates          | 99       |\n","|    policy_loss        | 319      |\n","|    std                | 0.996    |\n","|    value_loss         | 219      |\n","------------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 21.4     |\n","|    ep_rew_mean     | 104      |\n","| time/              |          |\n","|    fps             | 540      |\n","|    iterations      | 1        |\n","|    time_elapsed    | 3        |\n","|    total_timesteps | 2048     |\n","---------------------------------\n","30.0 %\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 28.2     |\n","|    ep_rew_mean     | 150      |\n","| time/              |          |\n","|    episodes        | 4        |\n","|    fps             | 321      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 113      |\n","| train/             |          |\n","|    actor_loss      | 8.69     |\n","|    critic_loss     | 520      |\n","|    ent_coef        | 1        |\n","|    ent_coef_loss   | -0.00155 |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 12       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 25.1     |\n","|    ep_rew_mean     | 130      |\n","| time/              |          |\n","|    episodes        | 8        |\n","|    fps             | 108      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 201      |\n","| train/             |          |\n","|    actor_loss      | -24.8    |\n","|    critic_loss     | 23.2     |\n","|    ent_coef        | 0.971    |\n","|    ent_coef_loss   | -0.818   |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 100      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 24.2     |\n","|    ep_rew_mean     | 125      |\n","| time/              |          |\n","|    episodes        | 12       |\n","|    fps             | 93       |\n","|    time_elapsed    | 3        |\n","|    total_timesteps | 291      |\n","| train/             |          |\n","|    actor_loss      | -28      |\n","|    critic_loss     | 15.8     |\n","|    ent_coef        | 0.943    |\n","|    ent_coef_loss   | -1.61    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 190      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 23.8     |\n","|    ep_rew_mean     | 122      |\n","| time/              |          |\n","|    episodes        | 16       |\n","|    fps             | 88       |\n","|    time_elapsed    | 4        |\n","|    total_timesteps | 380      |\n","| train/             |          |\n","|    actor_loss      | -33      |\n","|    critic_loss     | 31.3     |\n","|    ent_coef        | 0.917    |\n","|    ent_coef_loss   | -2.41    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 279      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 23.1     |\n","|    ep_rew_mean     | 118      |\n","| time/              |          |\n","|    episodes        | 20       |\n","|    fps             | 85       |\n","|    time_elapsed    | 5        |\n","|    total_timesteps | 462      |\n","| train/             |          |\n","|    actor_loss      | -36.4    |\n","|    critic_loss     | 12.4     |\n","|    ent_coef        | 0.894    |\n","|    ent_coef_loss   | -3.15    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 361      |\n","---------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 21.5     |\n","|    ep_rew_mean     | 104      |\n","| time/              |          |\n","|    episodes        | 4        |\n","|    fps             | 1182     |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 86       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 18.9     |\n","|    ep_rew_mean     | 89.6     |\n","| time/              |          |\n","|    episodes        | 8        |\n","|    fps             | 352      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 151      |\n","| train/             |          |\n","|    actor_loss      | -5.13    |\n","|    critic_loss     | 4.26     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 50       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 17.2     |\n","|    ep_rew_mean     | 80.7     |\n","| time/              |          |\n","|    episodes        | 12       |\n","|    fps             | 251      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 207      |\n","| train/             |          |\n","|    actor_loss      | -5.77    |\n","|    critic_loss     | 1.73     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 107      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 16.6     |\n","|    ep_rew_mean     | 77       |\n","| time/              |          |\n","|    episodes        | 16       |\n","|    fps             | 212      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 265      |\n","| train/             |          |\n","|    actor_loss      | -6.25    |\n","|    critic_loss     | 1.61     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 165      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 16.1     |\n","|    ep_rew_mean     | 74.2     |\n","| time/              |          |\n","|    episodes        | 20       |\n","|    fps             | 194      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 321      |\n","| train/             |          |\n","|    actor_loss      | -6.66    |\n","|    critic_loss     | 1.11     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 221      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 15.8     |\n","|    ep_rew_mean     | 72.6     |\n","| time/              |          |\n","|    episodes        | 24       |\n","|    fps             | 182      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 378      |\n","| train/             |          |\n","|    actor_loss      | -6.93    |\n","|    critic_loss     | 1.68     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 278      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 15.5     |\n","|    ep_rew_mean     | 71.3     |\n","| time/              |          |\n","|    episodes        | 28       |\n","|    fps             | 174      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 434      |\n","| train/             |          |\n","|    actor_loss      | -7.55    |\n","|    critic_loss     | 0.655    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 334      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 15.3     |\n","|    ep_rew_mean     | 70.3     |\n","| time/              |          |\n","|    episodes        | 32       |\n","|    fps             | 168      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 490      |\n","| train/             |          |\n","|    actor_loss      | -8.04    |\n","|    critic_loss     | 0.448    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 390      |\n","---------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","------------------------------------\n","| rollout/              |          |\n","|    ep_len_mean        | 25.4     |\n","|    ep_rew_mean        | 130      |\n","| time/                 |          |\n","|    fps                | 368      |\n","|    iterations         | 100      |\n","|    time_elapsed       | 1        |\n","|    total_timesteps    | 500      |\n","| train/                |          |\n","|    entropy_loss       | -24      |\n","|    explained_variance | 0.358    |\n","|    learning_rate      | 0.0007   |\n","|    n_updates          | 99       |\n","|    policy_loss        | 306      |\n","|    std                | 0.994    |\n","|    value_loss         | 196      |\n","------------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 21.8     |\n","|    ep_rew_mean     | 108      |\n","| time/              |          |\n","|    fps             | 423      |\n","|    iterations      | 1        |\n","|    time_elapsed    | 4        |\n","|    total_timesteps | 2048     |\n","---------------------------------\n","35.0 %\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 27.2     |\n","|    ep_rew_mean     | 138      |\n","| time/              |          |\n","|    episodes        | 4        |\n","|    fps             | 545      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 109      |\n","| train/             |          |\n","|    actor_loss      | 13.6     |\n","|    critic_loss     | 561      |\n","|    ent_coef        | 1        |\n","|    ent_coef_loss   | 0.0053   |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 8        |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 25.5     |\n","|    ep_rew_mean     | 130      |\n","| time/              |          |\n","|    episodes        | 8        |\n","|    fps             | 140      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 204      |\n","| train/             |          |\n","|    actor_loss      | -23.9    |\n","|    critic_loss     | 34.7     |\n","|    ent_coef        | 0.97     |\n","|    ent_coef_loss   | -0.831   |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 103      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 24       |\n","|    ep_rew_mean     | 122      |\n","| time/              |          |\n","|    episodes        | 12       |\n","|    fps             | 113      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 288      |\n","| train/             |          |\n","|    actor_loss      | -29.3    |\n","|    critic_loss     | 11.6     |\n","|    ent_coef        | 0.944    |\n","|    ent_coef_loss   | -1.63    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 187      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 22.4     |\n","|    ep_rew_mean     | 113      |\n","| time/              |          |\n","|    episodes        | 16       |\n","|    fps             | 98       |\n","|    time_elapsed    | 3        |\n","|    total_timesteps | 359      |\n","| train/             |          |\n","|    actor_loss      | -31.8    |\n","|    critic_loss     | 9.83     |\n","|    ent_coef        | 0.922    |\n","|    ent_coef_loss   | -2.27    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 258      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 23       |\n","|    ep_rew_mean     | 116      |\n","| time/              |          |\n","|    episodes        | 20       |\n","|    fps             | 85       |\n","|    time_elapsed    | 5        |\n","|    total_timesteps | 460      |\n","| train/             |          |\n","|    actor_loss      | -36.4    |\n","|    critic_loss     | 10.8     |\n","|    ent_coef        | 0.894    |\n","|    ent_coef_loss   | -3.15    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 359      |\n","---------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 23.2     |\n","|    ep_rew_mean     | 116      |\n","| time/              |          |\n","|    episodes        | 4        |\n","|    fps             | 1268     |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 93       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 21.6     |\n","|    ep_rew_mean     | 118      |\n","| time/              |          |\n","|    episodes        | 8        |\n","|    fps             | 315      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 173      |\n","| train/             |          |\n","|    actor_loss      | -6.32    |\n","|    critic_loss     | 4.75     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 63       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 20.5     |\n","|    ep_rew_mean     | 115      |\n","| time/              |          |\n","|    episodes        | 12       |\n","|    fps             | 228      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 246      |\n","| train/             |          |\n","|    actor_loss      | -7.61    |\n","|    critic_loss     | 1.41     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 134      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 20.4     |\n","|    ep_rew_mean     | 118      |\n","| time/              |          |\n","|    episodes        | 16       |\n","|    fps             | 197      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 327      |\n","| train/             |          |\n","|    actor_loss      | -8.52    |\n","|    critic_loss     | 0.676    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 213      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 20.4     |\n","|    ep_rew_mean     | 120      |\n","| time/              |          |\n","|    episodes        | 20       |\n","|    fps             | 181      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 408      |\n","| train/             |          |\n","|    actor_loss      | -9.46    |\n","|    critic_loss     | 0.661    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 295      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 20.3     |\n","|    ep_rew_mean     | 121      |\n","| time/              |          |\n","|    episodes        | 24       |\n","|    fps             | 171      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 488      |\n","| train/             |          |\n","|    actor_loss      | -10.5    |\n","|    critic_loss     | 1.14     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 375      |\n","---------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","------------------------------------\n","| rollout/              |          |\n","|    ep_len_mean        | 21.5     |\n","|    ep_rew_mean        | 102      |\n","| time/                 |          |\n","|    fps                | 264      |\n","|    iterations         | 100      |\n","|    time_elapsed       | 1        |\n","|    total_timesteps    | 500      |\n","| train/                |          |\n","|    entropy_loss       | -23.9    |\n","|    explained_variance | -0.194   |\n","|    learning_rate      | 0.0007   |\n","|    n_updates          | 99       |\n","|    policy_loss        | 185      |\n","|    std                | 0.99     |\n","|    value_loss         | 106      |\n","------------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 22.8     |\n","|    ep_rew_mean     | 113      |\n","| time/              |          |\n","|    fps             | 525      |\n","|    iterations      | 1        |\n","|    time_elapsed    | 3        |\n","|    total_timesteps | 2048     |\n","---------------------------------\n","40.0 %\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 18.8     |\n","|    ep_rew_mean     | 90.6     |\n","| time/              |          |\n","|    episodes        | 4        |\n","|    fps             | 1187     |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 75       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 24.1     |\n","|    ep_rew_mean     | 128      |\n","| time/              |          |\n","|    episodes        | 8        |\n","|    fps             | 146      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 193      |\n","| train/             |          |\n","|    actor_loss      | -26.7    |\n","|    critic_loss     | 14.9     |\n","|    ent_coef        | 0.972    |\n","|    ent_coef_loss   | -0.791   |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 92       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 23.2     |\n","|    ep_rew_mean     | 121      |\n","| time/              |          |\n","|    episodes        | 12       |\n","|    fps             | 114      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 279      |\n","| train/             |          |\n","|    actor_loss      | -31      |\n","|    critic_loss     | 11.4     |\n","|    ent_coef        | 0.946    |\n","|    ent_coef_loss   | -1.57    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 178      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 22.6     |\n","|    ep_rew_mean     | 116      |\n","| time/              |          |\n","|    episodes        | 16       |\n","|    fps             | 102      |\n","|    time_elapsed    | 3        |\n","|    total_timesteps | 362      |\n","| train/             |          |\n","|    actor_loss      | -34.3    |\n","|    critic_loss     | 6.64     |\n","|    ent_coef        | 0.921    |\n","|    ent_coef_loss   | -2.29    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 261      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 21.9     |\n","|    ep_rew_mean     | 111      |\n","| time/              |          |\n","|    episodes        | 20       |\n","|    fps             | 96       |\n","|    time_elapsed    | 4        |\n","|    total_timesteps | 437      |\n","| train/             |          |\n","|    actor_loss      | -37.2    |\n","|    critic_loss     | 6.62     |\n","|    ent_coef        | 0.9      |\n","|    ent_coef_loss   | -2.97    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 336      |\n","---------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 34.8     |\n","|    ep_rew_mean     | 198      |\n","| time/              |          |\n","|    episodes        | 4        |\n","|    fps             | 955      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 139      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 36.8     |\n","|    ep_rew_mean     | 244      |\n","| time/              |          |\n","|    episodes        | 8        |\n","|    fps             | 222      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 294      |\n","| train/             |          |\n","|    actor_loss      | -10.4    |\n","|    critic_loss     | 2.88     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 169      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 37.2     |\n","|    ep_rew_mean     | 258      |\n","| time/              |          |\n","|    episodes        | 12       |\n","|    fps             | 182      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 446      |\n","| train/             |          |\n","|    actor_loss      | -13.6    |\n","|    critic_loss     | 3.17     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 323      |\n","---------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","------------------------------------\n","| rollout/              |          |\n","|    ep_len_mean        | 23.9     |\n","|    ep_rew_mean        | 116      |\n","| time/                 |          |\n","|    fps                | 262      |\n","|    iterations         | 100      |\n","|    time_elapsed       | 1        |\n","|    total_timesteps    | 500      |\n","| train/                |          |\n","|    entropy_loss       | -24      |\n","|    explained_variance | 0.0391   |\n","|    learning_rate      | 0.0007   |\n","|    n_updates          | 99       |\n","|    policy_loss        | 326      |\n","|    std                | 0.99     |\n","|    value_loss         | 281      |\n","------------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 22.2     |\n","|    ep_rew_mean     | 110      |\n","| time/              |          |\n","|    fps             | 520      |\n","|    iterations      | 1        |\n","|    time_elapsed    | 3        |\n","|    total_timesteps | 2048     |\n","---------------------------------\n","45.0 %\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 21.5     |\n","|    ep_rew_mean     | 108      |\n","| time/              |          |\n","|    episodes        | 4        |\n","|    fps             | 1128     |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 86       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 20.8     |\n","|    ep_rew_mean     | 102      |\n","| time/              |          |\n","|    episodes        | 8        |\n","|    fps             | 174      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 166      |\n","| train/             |          |\n","|    actor_loss      | -25.9    |\n","|    critic_loss     | 20       |\n","|    ent_coef        | 0.981    |\n","|    ent_coef_loss   | -0.526   |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 65       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 21.4     |\n","|    ep_rew_mean     | 106      |\n","| time/              |          |\n","|    episodes        | 12       |\n","|    fps             | 120      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 257      |\n","| train/             |          |\n","|    actor_loss      | -30      |\n","|    critic_loss     | 10.2     |\n","|    ent_coef        | 0.953    |\n","|    ent_coef_loss   | -1.34    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 156      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 20.9     |\n","|    ep_rew_mean     | 103      |\n","| time/              |          |\n","|    episodes        | 16       |\n","|    fps             | 106      |\n","|    time_elapsed    | 3        |\n","|    total_timesteps | 334      |\n","| train/             |          |\n","|    actor_loss      | -32.9    |\n","|    critic_loss     | 8.16     |\n","|    ent_coef        | 0.93     |\n","|    ent_coef_loss   | -2.03    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 233      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 20.2     |\n","|    ep_rew_mean     | 99.8     |\n","| time/              |          |\n","|    episodes        | 20       |\n","|    fps             | 99       |\n","|    time_elapsed    | 4        |\n","|    total_timesteps | 405      |\n","| train/             |          |\n","|    actor_loss      | -35.3    |\n","|    critic_loss     | 17.4     |\n","|    ent_coef        | 0.91     |\n","|    ent_coef_loss   | -2.61    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 304      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 19.9     |\n","|    ep_rew_mean     | 97.4     |\n","| time/              |          |\n","|    episodes        | 24       |\n","|    fps             | 95       |\n","|    time_elapsed    | 5        |\n","|    total_timesteps | 477      |\n","| train/             |          |\n","|    actor_loss      | -38.7    |\n","|    critic_loss     | 7.01     |\n","|    ent_coef        | 0.891    |\n","|    ent_coef_loss   | -3.27    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 376      |\n","---------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 20.5     |\n","|    ep_rew_mean     | 101      |\n","| time/              |          |\n","|    episodes        | 4        |\n","|    fps             | 815      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 82       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 18.9     |\n","|    ep_rew_mean     | 91.4     |\n","| time/              |          |\n","|    episodes        | 8        |\n","|    fps             | 225      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 151      |\n","| train/             |          |\n","|    actor_loss      | -5.35    |\n","|    critic_loss     | 5.65     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 56       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 17.5     |\n","|    ep_rew_mean     | 84.2     |\n","| time/              |          |\n","|    episodes        | 12       |\n","|    fps             | 171      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 210      |\n","| train/             |          |\n","|    actor_loss      | -6.39    |\n","|    critic_loss     | 2.31     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 113      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 16.9     |\n","|    ep_rew_mean     | 81       |\n","| time/              |          |\n","|    episodes        | 16       |\n","|    fps             | 148      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 270      |\n","| train/             |          |\n","|    actor_loss      | -7.39    |\n","|    critic_loss     | 1        |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 173      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 16.5     |\n","|    ep_rew_mean     | 78.9     |\n","| time/              |          |\n","|    episodes        | 20       |\n","|    fps             | 138      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 330      |\n","| train/             |          |\n","|    actor_loss      | -8.26    |\n","|    critic_loss     | 0.873    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 233      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 16.2     |\n","|    ep_rew_mean     | 77.5     |\n","| time/              |          |\n","|    episodes        | 24       |\n","|    fps             | 139      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 390      |\n","| train/             |          |\n","|    actor_loss      | -8.88    |\n","|    critic_loss     | 0.51     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 293      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 16.1     |\n","|    ep_rew_mean     | 76.4     |\n","| time/              |          |\n","|    episodes        | 28       |\n","|    fps             | 138      |\n","|    time_elapsed    | 3        |\n","|    total_timesteps | 450      |\n","| train/             |          |\n","|    actor_loss      | -9.39    |\n","|    critic_loss     | 0.295    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 353      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 15.9     |\n","|    ep_rew_mean     | 75.7     |\n","| time/              |          |\n","|    episodes        | 32       |\n","|    fps             | 138      |\n","|    time_elapsed    | 3        |\n","|    total_timesteps | 510      |\n","| train/             |          |\n","|    actor_loss      | -9.98    |\n","|    critic_loss     | 0.234    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 413      |\n","---------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","------------------------------------\n","| rollout/              |          |\n","|    ep_len_mean        | 23.5     |\n","|    ep_rew_mean        | 111      |\n","| time/                 |          |\n","|    fps                | 363      |\n","|    iterations         | 100      |\n","|    time_elapsed       | 1        |\n","|    total_timesteps    | 500      |\n","| train/                |          |\n","|    entropy_loss       | -24      |\n","|    explained_variance | -0.335   |\n","|    learning_rate      | 0.0007   |\n","|    n_updates          | 99       |\n","|    policy_loss        | 146      |\n","|    std                | 0.992    |\n","|    value_loss         | 72.8     |\n","------------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 20.9     |\n","|    ep_rew_mean     | 103      |\n","| time/              |          |\n","|    fps             | 537      |\n","|    iterations      | 1        |\n","|    time_elapsed    | 3        |\n","|    total_timesteps | 2048     |\n","---------------------------------\n","50.0 %\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 19       |\n","|    ep_rew_mean     | 93.1     |\n","| time/              |          |\n","|    episodes        | 4        |\n","|    fps             | 724      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 76       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 22.6     |\n","|    ep_rew_mean     | 115      |\n","| time/              |          |\n","|    episodes        | 8        |\n","|    fps             | 119      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 181      |\n","| train/             |          |\n","|    actor_loss      | -27      |\n","|    critic_loss     | 13.2     |\n","|    ent_coef        | 0.975    |\n","|    ent_coef_loss   | -0.707   |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 80       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 23.2     |\n","|    ep_rew_mean     | 118      |\n","| time/              |          |\n","|    episodes        | 12       |\n","|    fps             | 87       |\n","|    time_elapsed    | 3        |\n","|    total_timesteps | 278      |\n","| train/             |          |\n","|    actor_loss      | -30.7    |\n","|    critic_loss     | 18.4     |\n","|    ent_coef        | 0.946    |\n","|    ent_coef_loss   | -1.55    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 177      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 23.9     |\n","|    ep_rew_mean     | 121      |\n","| time/              |          |\n","|    episodes        | 16       |\n","|    fps             | 84       |\n","|    time_elapsed    | 4        |\n","|    total_timesteps | 383      |\n","| train/             |          |\n","|    actor_loss      | -36.4    |\n","|    critic_loss     | 7.25     |\n","|    ent_coef        | 0.915    |\n","|    ent_coef_loss   | -2.48    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 282      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 23.7     |\n","|    ep_rew_mean     | 120      |\n","| time/              |          |\n","|    episodes        | 20       |\n","|    fps             | 82       |\n","|    time_elapsed    | 5        |\n","|    total_timesteps | 474      |\n","| train/             |          |\n","|    actor_loss      | -39.1    |\n","|    critic_loss     | 6.97     |\n","|    ent_coef        | 0.89     |\n","|    ent_coef_loss   | -3.26    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 373      |\n","---------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 23.2     |\n","|    ep_rew_mean     | 119      |\n","| time/              |          |\n","|    episodes        | 4        |\n","|    fps             | 1357     |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 93       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 24.6     |\n","|    ep_rew_mean     | 148      |\n","| time/              |          |\n","|    episodes        | 8        |\n","|    fps             | 270      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 197      |\n","| train/             |          |\n","|    actor_loss      | -9.38    |\n","|    critic_loss     | 1.81     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 89       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 21.3     |\n","|    ep_rew_mean     | 124      |\n","| time/              |          |\n","|    episodes        | 12       |\n","|    fps             | 197      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 256      |\n","| train/             |          |\n","|    actor_loss      | -10.5    |\n","|    critic_loss     | 0.973    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 149      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 19.5     |\n","|    ep_rew_mean     | 113      |\n","| time/              |          |\n","|    episodes        | 16       |\n","|    fps             | 169      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 312      |\n","| train/             |          |\n","|    actor_loss      | -11      |\n","|    critic_loss     | 0.791    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 205      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 18.6     |\n","|    ep_rew_mean     | 107      |\n","| time/              |          |\n","|    episodes        | 20       |\n","|    fps             | 153      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 371      |\n","| train/             |          |\n","|    actor_loss      | -11.4    |\n","|    critic_loss     | 1.18     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 264      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 18.2     |\n","|    ep_rew_mean     | 102      |\n","| time/              |          |\n","|    episodes        | 24       |\n","|    fps             | 143      |\n","|    time_elapsed    | 3        |\n","|    total_timesteps | 436      |\n","| train/             |          |\n","|    actor_loss      | -12      |\n","|    critic_loss     | 7.45     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 326      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 18.1     |\n","|    ep_rew_mean     | 100      |\n","| time/              |          |\n","|    episodes        | 28       |\n","|    fps             | 135      |\n","|    time_elapsed    | 3        |\n","|    total_timesteps | 508      |\n","| train/             |          |\n","|    actor_loss      | -12.8    |\n","|    critic_loss     | 8.59     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 398      |\n","---------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","------------------------------------\n","| rollout/              |          |\n","|    ep_len_mean        | 19.4     |\n","|    ep_rew_mean        | 92.8     |\n","| time/                 |          |\n","|    fps                | 273      |\n","|    iterations         | 100      |\n","|    time_elapsed       | 1        |\n","|    total_timesteps    | 500      |\n","| train/                |          |\n","|    entropy_loss       | -24.1    |\n","|    explained_variance | 0.0523   |\n","|    learning_rate      | 0.0007   |\n","|    n_updates          | 99       |\n","|    policy_loss        | 340      |\n","|    std                | 0.996    |\n","|    value_loss         | 238      |\n","------------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 22       |\n","|    ep_rew_mean     | 108      |\n","| time/              |          |\n","|    fps             | 524      |\n","|    iterations      | 1        |\n","|    time_elapsed    | 3        |\n","|    total_timesteps | 2048     |\n","---------------------------------\n","55.00000000000001 %\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 20.8     |\n","|    ep_rew_mean     | 102      |\n","| time/              |          |\n","|    episodes        | 4        |\n","|    fps             | 1115     |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 83       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 20.8     |\n","|    ep_rew_mean     | 103      |\n","| time/              |          |\n","|    episodes        | 8        |\n","|    fps             | 169      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 166      |\n","| train/             |          |\n","|    actor_loss      | -24.7    |\n","|    critic_loss     | 17.4     |\n","|    ent_coef        | 0.981    |\n","|    ent_coef_loss   | -0.54    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 65       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 22.5     |\n","|    ep_rew_mean     | 109      |\n","| time/              |          |\n","|    episodes        | 12       |\n","|    fps             | 113      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 270      |\n","| train/             |          |\n","|    actor_loss      | -29      |\n","|    critic_loss     | 10.3     |\n","|    ent_coef        | 0.949    |\n","|    ent_coef_loss   | -1.45    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 169      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 23       |\n","|    ep_rew_mean     | 113      |\n","| time/              |          |\n","|    episodes        | 16       |\n","|    fps             | 99       |\n","|    time_elapsed    | 3        |\n","|    total_timesteps | 368      |\n","| train/             |          |\n","|    actor_loss      | -33.7    |\n","|    critic_loss     | 7.34     |\n","|    ent_coef        | 0.92     |\n","|    ent_coef_loss   | -2.32    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 267      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 22.9     |\n","|    ep_rew_mean     | 112      |\n","| time/              |          |\n","|    episodes        | 20       |\n","|    fps             | 92       |\n","|    time_elapsed    | 4        |\n","|    total_timesteps | 457      |\n","| train/             |          |\n","|    actor_loss      | -37      |\n","|    critic_loss     | 9.35     |\n","|    ent_coef        | 0.895    |\n","|    ent_coef_loss   | -3.1     |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 356      |\n","---------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 20       |\n","|    ep_rew_mean     | 98.4     |\n","| time/              |          |\n","|    episodes        | 4        |\n","|    fps             | 779      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 80       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 17.9     |\n","|    ep_rew_mean     | 91.1     |\n","| time/              |          |\n","|    episodes        | 8        |\n","|    fps             | 316      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 143      |\n","| train/             |          |\n","|    actor_loss      | -4.35    |\n","|    critic_loss     | 5.7      |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 32       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 16.6     |\n","|    ep_rew_mean     | 88.6     |\n","| time/              |          |\n","|    episodes        | 12       |\n","|    fps             | 197      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 199      |\n","| train/             |          |\n","|    actor_loss      | -5.69    |\n","|    critic_loss     | 6.29     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 87       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 16.3     |\n","|    ep_rew_mean     | 89.8     |\n","| time/              |          |\n","|    episodes        | 16       |\n","|    fps             | 166      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 261      |\n","| train/             |          |\n","|    actor_loss      | -7.52    |\n","|    critic_loss     | 0.748    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 146      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 16.2     |\n","|    ep_rew_mean     | 91.1     |\n","| time/              |          |\n","|    episodes        | 20       |\n","|    fps             | 158      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 325      |\n","| train/             |          |\n","|    actor_loss      | -9.2     |\n","|    critic_loss     | 1.04     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 211      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 16.2     |\n","|    ep_rew_mean     | 92.1     |\n","| time/              |          |\n","|    episodes        | 24       |\n","|    fps             | 153      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 389      |\n","| train/             |          |\n","|    actor_loss      | -10.5    |\n","|    critic_loss     | 0.935    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 275      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 16.2     |\n","|    ep_rew_mean     | 92.9     |\n","| time/              |          |\n","|    episodes        | 28       |\n","|    fps             | 150      |\n","|    time_elapsed    | 3        |\n","|    total_timesteps | 453      |\n","| train/             |          |\n","|    actor_loss      | -11.6    |\n","|    critic_loss     | 0.823    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 339      |\n","---------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","------------------------------------\n","| rollout/              |          |\n","|    ep_len_mean        | 20.1     |\n","|    ep_rew_mean        | 96.4     |\n","| time/                 |          |\n","|    fps                | 262      |\n","|    iterations         | 100      |\n","|    time_elapsed       | 1        |\n","|    total_timesteps    | 500      |\n","| train/                |          |\n","|    entropy_loss       | -24      |\n","|    explained_variance | 0.0425   |\n","|    learning_rate      | 0.0007   |\n","|    n_updates          | 99       |\n","|    policy_loss        | 328      |\n","|    std                | 0.995    |\n","|    value_loss         | 226      |\n","------------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 21.8     |\n","|    ep_rew_mean     | 108      |\n","| time/              |          |\n","|    fps             | 526      |\n","|    iterations      | 1        |\n","|    time_elapsed    | 3        |\n","|    total_timesteps | 2048     |\n","---------------------------------\n","60.0 %\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 24.2     |\n","|    ep_rew_mean     | 121      |\n","| time/              |          |\n","|    episodes        | 4        |\n","|    fps             | 1040     |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 97       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 28       |\n","|    ep_rew_mean     | 148      |\n","| time/              |          |\n","|    episodes        | 8        |\n","|    fps             | 124      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 224      |\n","| train/             |          |\n","|    actor_loss      | -29.5    |\n","|    critic_loss     | 11.3     |\n","|    ent_coef        | 0.962    |\n","|    ent_coef_loss   | -1.07    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 123      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 26.8     |\n","|    ep_rew_mean     | 141      |\n","| time/              |          |\n","|    episodes        | 12       |\n","|    fps             | 103      |\n","|    time_elapsed    | 3        |\n","|    total_timesteps | 322      |\n","| train/             |          |\n","|    actor_loss      | -33      |\n","|    critic_loss     | 22.5     |\n","|    ent_coef        | 0.933    |\n","|    ent_coef_loss   | -1.94    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 221      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 26.1     |\n","|    ep_rew_mean     | 133      |\n","| time/              |          |\n","|    episodes        | 16       |\n","|    fps             | 95       |\n","|    time_elapsed    | 4        |\n","|    total_timesteps | 417      |\n","| train/             |          |\n","|    actor_loss      | -36.7    |\n","|    critic_loss     | 13.5     |\n","|    ent_coef        | 0.906    |\n","|    ent_coef_loss   | -2.77    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 316      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 24.6     |\n","|    ep_rew_mean     | 125      |\n","| time/              |          |\n","|    episodes        | 20       |\n","|    fps             | 91       |\n","|    time_elapsed    | 5        |\n","|    total_timesteps | 492      |\n","| train/             |          |\n","|    actor_loss      | -40.1    |\n","|    critic_loss     | 10.7     |\n","|    ent_coef        | 0.885    |\n","|    ent_coef_loss   | -3.41    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 391      |\n","---------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 25       |\n","|    ep_rew_mean     | 128      |\n","| time/              |          |\n","|    episodes        | 4        |\n","|    fps             | 1239     |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 100      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 25.8     |\n","|    ep_rew_mean     | 137      |\n","| time/              |          |\n","|    episodes        | 8        |\n","|    fps             | 290      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 206      |\n","| train/             |          |\n","|    actor_loss      | -7.08    |\n","|    critic_loss     | 3.99     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 79       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 26.4     |\n","|    ep_rew_mean     | 157      |\n","| time/              |          |\n","|    episodes        | 12       |\n","|    fps             | 190      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 317      |\n","| train/             |          |\n","|    actor_loss      | -9.56    |\n","|    critic_loss     | 1.97     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 187      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 24.1     |\n","|    ep_rew_mean     | 145      |\n","| time/              |          |\n","|    episodes        | 16       |\n","|    fps             | 155      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 385      |\n","| train/             |          |\n","|    actor_loss      | -11.1    |\n","|    critic_loss     | 2.08     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 272      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 21.9     |\n","|    ep_rew_mean     | 130      |\n","| time/              |          |\n","|    episodes        | 20       |\n","|    fps             | 146      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 437      |\n","| train/             |          |\n","|    actor_loss      | -11.7    |\n","|    critic_loss     | 2.02     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 324      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 20.4     |\n","|    ep_rew_mean     | 120      |\n","| time/              |          |\n","|    episodes        | 24       |\n","|    fps             | 140      |\n","|    time_elapsed    | 3        |\n","|    total_timesteps | 489      |\n","| train/             |          |\n","|    actor_loss      | -12.3    |\n","|    critic_loss     | 2.26     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 376      |\n","---------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","------------------------------------\n","| rollout/              |          |\n","|    ep_len_mean        | 21.1     |\n","|    ep_rew_mean        | 102      |\n","| time/                 |          |\n","|    fps                | 355      |\n","|    iterations         | 100      |\n","|    time_elapsed       | 1        |\n","|    total_timesteps    | 500      |\n","| train/                |          |\n","|    entropy_loss       | -24.1    |\n","|    explained_variance | -0.00499 |\n","|    learning_rate      | 0.0007   |\n","|    n_updates          | 99       |\n","|    policy_loss        | 322      |\n","|    std                | 1        |\n","|    value_loss         | 224      |\n","------------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 21.7     |\n","|    ep_rew_mean     | 108      |\n","| time/              |          |\n","|    fps             | 458      |\n","|    iterations      | 1        |\n","|    time_elapsed    | 4        |\n","|    total_timesteps | 2048     |\n","---------------------------------\n","65.0 %\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 28.8     |\n","|    ep_rew_mean     | 152      |\n","| time/              |          |\n","|    episodes        | 4        |\n","|    fps             | 411      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 115      |\n","| train/             |          |\n","|    actor_loss      | 3.98     |\n","|    critic_loss     | 389      |\n","|    ent_coef        | 1        |\n","|    ent_coef_loss   | 0.00159  |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 14       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 23.9     |\n","|    ep_rew_mean     | 122      |\n","| time/              |          |\n","|    episodes        | 8        |\n","|    fps             | 149      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 191      |\n","| train/             |          |\n","|    actor_loss      | -26.8    |\n","|    critic_loss     | 16.7     |\n","|    ent_coef        | 0.975    |\n","|    ent_coef_loss   | -0.703   |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 90       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 23.1     |\n","|    ep_rew_mean     | 116      |\n","| time/              |          |\n","|    episodes        | 12       |\n","|    fps             | 114      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 277      |\n","| train/             |          |\n","|    actor_loss      | -31.5    |\n","|    critic_loss     | 6.93     |\n","|    ent_coef        | 0.948    |\n","|    ent_coef_loss   | -1.49    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 176      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 22.3     |\n","|    ep_rew_mean     | 111      |\n","| time/              |          |\n","|    episodes        | 16       |\n","|    fps             | 102      |\n","|    time_elapsed    | 3        |\n","|    total_timesteps | 357      |\n","| train/             |          |\n","|    actor_loss      | -33.2    |\n","|    critic_loss     | 14.8     |\n","|    ent_coef        | 0.925    |\n","|    ent_coef_loss   | -2.19    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 256      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 23.2     |\n","|    ep_rew_mean     | 116      |\n","| time/              |          |\n","|    episodes        | 20       |\n","|    fps             | 88       |\n","|    time_elapsed    | 5        |\n","|    total_timesteps | 464      |\n","| train/             |          |\n","|    actor_loss      | -37.7    |\n","|    critic_loss     | 12.5     |\n","|    ent_coef        | 0.895    |\n","|    ent_coef_loss   | -3.13    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 363      |\n","---------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 23.5     |\n","|    ep_rew_mean     | 120      |\n","| time/              |          |\n","|    episodes        | 4        |\n","|    fps             | 1273     |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 94       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 19.8     |\n","|    ep_rew_mean     | 101      |\n","| time/              |          |\n","|    episodes        | 8        |\n","|    fps             | 347      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 158      |\n","| train/             |          |\n","|    actor_loss      | -5.72    |\n","|    critic_loss     | 3.01     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 49       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 18.2     |\n","|    ep_rew_mean     | 93.7     |\n","| time/              |          |\n","|    episodes        | 12       |\n","|    fps             | 241      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 219      |\n","| train/             |          |\n","|    actor_loss      | -6.8     |\n","|    critic_loss     | 0.325    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 109      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 17.7     |\n","|    ep_rew_mean     | 91.8     |\n","| time/              |          |\n","|    episodes        | 16       |\n","|    fps             | 203      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 283      |\n","| train/             |          |\n","|    actor_loss      | -7.78    |\n","|    critic_loss     | 0.329    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 173      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 17.4     |\n","|    ep_rew_mean     | 91.3     |\n","| time/              |          |\n","|    episodes        | 20       |\n","|    fps             | 184      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 347      |\n","| train/             |          |\n","|    actor_loss      | -8.63    |\n","|    critic_loss     | 0.221    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 237      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 17.1     |\n","|    ep_rew_mean     | 90.8     |\n","| time/              |          |\n","|    episodes        | 24       |\n","|    fps             | 174      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 411      |\n","| train/             |          |\n","|    actor_loss      | -9.49    |\n","|    critic_loss     | 0.548    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 301      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 17       |\n","|    ep_rew_mean     | 90.4     |\n","| time/              |          |\n","|    episodes        | 28       |\n","|    fps             | 168      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 475      |\n","| train/             |          |\n","|    actor_loss      | -10.1    |\n","|    critic_loss     | 0.181    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 365      |\n","---------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","------------------------------------\n","| rollout/              |          |\n","|    ep_len_mean        | 22       |\n","|    ep_rew_mean        | 107      |\n","| time/                 |          |\n","|    fps                | 363      |\n","|    iterations         | 100      |\n","|    time_elapsed       | 1        |\n","|    total_timesteps    | 500      |\n","| train/                |          |\n","|    entropy_loss       | -24.1    |\n","|    explained_variance | -0.148   |\n","|    learning_rate      | 0.0007   |\n","|    n_updates          | 99       |\n","|    policy_loss        | 378      |\n","|    std                | 1        |\n","|    value_loss         | 258      |\n","------------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 22.7     |\n","|    ep_rew_mean     | 113      |\n","| time/              |          |\n","|    fps             | 420      |\n","|    iterations      | 1        |\n","|    time_elapsed    | 4        |\n","|    total_timesteps | 2048     |\n","---------------------------------\n","70.0 %\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 26       |\n","|    ep_rew_mean     | 134      |\n","| time/              |          |\n","|    episodes        | 4        |\n","|    fps             | 754      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 104      |\n","| train/             |          |\n","|    actor_loss      | 13.8     |\n","|    critic_loss     | 1.18e+03 |\n","|    ent_coef        | 1        |\n","|    ent_coef_loss   | 0.00446  |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 3        |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 25.2     |\n","|    ep_rew_mean     | 127      |\n","| time/              |          |\n","|    episodes        | 8        |\n","|    fps             | 108      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 202      |\n","| train/             |          |\n","|    actor_loss      | -25.9    |\n","|    critic_loss     | 15.5     |\n","|    ent_coef        | 0.97     |\n","|    ent_coef_loss   | -0.853   |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 101      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 24.5     |\n","|    ep_rew_mean     | 123      |\n","| time/              |          |\n","|    episodes        | 12       |\n","|    fps             | 84       |\n","|    time_elapsed    | 3        |\n","|    total_timesteps | 294      |\n","| train/             |          |\n","|    actor_loss      | -29.8    |\n","|    critic_loss     | 10.6     |\n","|    ent_coef        | 0.942    |\n","|    ent_coef_loss   | -1.67    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 193      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 24       |\n","|    ep_rew_mean     | 122      |\n","| time/              |          |\n","|    episodes        | 16       |\n","|    fps             | 80       |\n","|    time_elapsed    | 4        |\n","|    total_timesteps | 384      |\n","| train/             |          |\n","|    actor_loss      | -35.1    |\n","|    critic_loss     | 6.03     |\n","|    ent_coef        | 0.916    |\n","|    ent_coef_loss   | -2.47    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 283      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 22.9     |\n","|    ep_rew_mean     | 115      |\n","| time/              |          |\n","|    episodes        | 20       |\n","|    fps             | 79       |\n","|    time_elapsed    | 5        |\n","|    total_timesteps | 458      |\n","| train/             |          |\n","|    actor_loss      | -37.8    |\n","|    critic_loss     | 8.9      |\n","|    ent_coef        | 0.895    |\n","|    ent_coef_loss   | -3.12    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 357      |\n","---------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 28.5     |\n","|    ep_rew_mean     | 148      |\n","| time/              |          |\n","|    episodes        | 4        |\n","|    fps             | 1157     |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 114      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 23.5     |\n","|    ep_rew_mean     | 127      |\n","| time/              |          |\n","|    episodes        | 8        |\n","|    fps             | 280      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 188      |\n","| train/             |          |\n","|    actor_loss      | -6.89    |\n","|    critic_loss     | 0.869    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 78       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 21       |\n","|    ep_rew_mean     | 113      |\n","| time/              |          |\n","|    episodes        | 12       |\n","|    fps             | 220      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 252      |\n","| train/             |          |\n","|    actor_loss      | -8.29    |\n","|    critic_loss     | 0.385    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 142      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 19.8     |\n","|    ep_rew_mean     | 106      |\n","| time/              |          |\n","|    episodes        | 16       |\n","|    fps             | 197      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 316      |\n","| train/             |          |\n","|    actor_loss      | -9.19    |\n","|    critic_loss     | 0.728    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 206      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 19       |\n","|    ep_rew_mean     | 102      |\n","| time/              |          |\n","|    episodes        | 20       |\n","|    fps             | 184      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 380      |\n","| train/             |          |\n","|    actor_loss      | -10.1    |\n","|    critic_loss     | 0.328    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 270      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 18.5     |\n","|    ep_rew_mean     | 99.8     |\n","| time/              |          |\n","|    episodes        | 24       |\n","|    fps             | 175      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 444      |\n","| train/             |          |\n","|    actor_loss      | -10.9    |\n","|    critic_loss     | 0.393    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 334      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 18.1     |\n","|    ep_rew_mean     | 97.8     |\n","| time/              |          |\n","|    episodes        | 28       |\n","|    fps             | 162      |\n","|    time_elapsed    | 3        |\n","|    total_timesteps | 508      |\n","| train/             |          |\n","|    actor_loss      | -11.7    |\n","|    critic_loss     | 0.469    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 398      |\n","---------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","------------------------------------\n","| rollout/              |          |\n","|    ep_len_mean        | 24.9     |\n","|    ep_rew_mean        | 121      |\n","| time/                 |          |\n","|    fps                | 359      |\n","|    iterations         | 100      |\n","|    time_elapsed       | 1        |\n","|    total_timesteps    | 500      |\n","| train/                |          |\n","|    entropy_loss       | -24.1    |\n","|    explained_variance | -0.75    |\n","|    learning_rate      | 0.0007   |\n","|    n_updates          | 99       |\n","|    policy_loss        | 329      |\n","|    std                | 1        |\n","|    value_loss         | 274      |\n","------------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 21.2     |\n","|    ep_rew_mean     | 103      |\n","| time/              |          |\n","|    fps             | 520      |\n","|    iterations      | 1        |\n","|    time_elapsed    | 3        |\n","|    total_timesteps | 2048     |\n","---------------------------------\n","75.0 %\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 23       |\n","|    ep_rew_mean     | 112      |\n","| time/              |          |\n","|    episodes        | 4        |\n","|    fps             | 528      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 92       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 24.6     |\n","|    ep_rew_mean     | 122      |\n","| time/              |          |\n","|    episodes        | 8        |\n","|    fps             | 107      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 197      |\n","| train/             |          |\n","|    actor_loss      | -27.1    |\n","|    critic_loss     | 16.3     |\n","|    ent_coef        | 0.97     |\n","|    ent_coef_loss   | -0.85    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 96       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 24.6     |\n","|    ep_rew_mean     | 124      |\n","| time/              |          |\n","|    episodes        | 12       |\n","|    fps             | 90       |\n","|    time_elapsed    | 3        |\n","|    total_timesteps | 295      |\n","| train/             |          |\n","|    actor_loss      | -31.9    |\n","|    critic_loss     | 11.5     |\n","|    ent_coef        | 0.94     |\n","|    ent_coef_loss   | -1.72    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 194      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 23.7     |\n","|    ep_rew_mean     | 119      |\n","| time/              |          |\n","|    episodes        | 16       |\n","|    fps             | 86       |\n","|    time_elapsed    | 4        |\n","|    total_timesteps | 379      |\n","| train/             |          |\n","|    actor_loss      | -35.3    |\n","|    critic_loss     | 5.74     |\n","|    ent_coef        | 0.916    |\n","|    ent_coef_loss   | -2.46    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 278      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 22.9     |\n","|    ep_rew_mean     | 114      |\n","| time/              |          |\n","|    episodes        | 20       |\n","|    fps             | 84       |\n","|    time_elapsed    | 5        |\n","|    total_timesteps | 459      |\n","| train/             |          |\n","|    actor_loss      | -38.8    |\n","|    critic_loss     | 7.7      |\n","|    ent_coef        | 0.894    |\n","|    ent_coef_loss   | -3.15    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 358      |\n","---------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 26.5     |\n","|    ep_rew_mean     | 137      |\n","| time/              |          |\n","|    episodes        | 4        |\n","|    fps             | 971      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 106      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 21.1     |\n","|    ep_rew_mean     | 109      |\n","| time/              |          |\n","|    episodes        | 8        |\n","|    fps             | 265      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 169      |\n","| train/             |          |\n","|    actor_loss      | -6.44    |\n","|    critic_loss     | 1.95     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 73       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 20.2     |\n","|    ep_rew_mean     | 106      |\n","| time/              |          |\n","|    episodes        | 12       |\n","|    fps             | 201      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 243      |\n","| train/             |          |\n","|    actor_loss      | -7.86    |\n","|    critic_loss     | 5.63     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 148      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 19.4     |\n","|    ep_rew_mean     | 102      |\n","| time/              |          |\n","|    episodes        | 16       |\n","|    fps             | 181      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 311      |\n","| train/             |          |\n","|    actor_loss      | -8.77    |\n","|    critic_loss     | 3.29     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 216      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 19.1     |\n","|    ep_rew_mean     | 101      |\n","| time/              |          |\n","|    episodes        | 20       |\n","|    fps             | 155      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 382      |\n","| train/             |          |\n","|    actor_loss      | -9.47    |\n","|    critic_loss     | 0.843    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 287      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 18.6     |\n","|    ep_rew_mean     | 97.4     |\n","| time/              |          |\n","|    episodes        | 24       |\n","|    fps             | 143      |\n","|    time_elapsed    | 3        |\n","|    total_timesteps | 447      |\n","| train/             |          |\n","|    actor_loss      | -9.93    |\n","|    critic_loss     | 0.76     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 349      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 18.2     |\n","|    ep_rew_mean     | 94.5     |\n","| time/              |          |\n","|    episodes        | 28       |\n","|    fps             | 134      |\n","|    time_elapsed    | 3        |\n","|    total_timesteps | 509      |\n","| train/             |          |\n","|    actor_loss      | -10.4    |\n","|    critic_loss     | 0.684    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 413      |\n","---------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","------------------------------------\n","| rollout/              |          |\n","|    ep_len_mean        | 21       |\n","|    ep_rew_mean        | 105      |\n","| time/                 |          |\n","|    fps                | 304      |\n","|    iterations         | 100      |\n","|    time_elapsed       | 1        |\n","|    total_timesteps    | 500      |\n","| train/                |          |\n","|    entropy_loss       | -24.1    |\n","|    explained_variance | 0.251    |\n","|    learning_rate      | 0.0007   |\n","|    n_updates          | 99       |\n","|    policy_loss        | 254      |\n","|    std                | 0.997    |\n","|    value_loss         | 161      |\n","------------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 23.4     |\n","|    ep_rew_mean     | 117      |\n","| time/              |          |\n","|    fps             | 525      |\n","|    iterations      | 1        |\n","|    time_elapsed    | 3        |\n","|    total_timesteps | 2048     |\n","---------------------------------\n","80.0 %\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 21       |\n","|    ep_rew_mean     | 102      |\n","| time/              |          |\n","|    episodes        | 4        |\n","|    fps             | 1178     |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 84       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 27.6     |\n","|    ep_rew_mean     | 140      |\n","| time/              |          |\n","|    episodes        | 8        |\n","|    fps             | 133      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 221      |\n","| train/             |          |\n","|    actor_loss      | -25.3    |\n","|    critic_loss     | 25.2     |\n","|    ent_coef        | 0.962    |\n","|    ent_coef_loss   | -1.03    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 120      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 24.4     |\n","|    ep_rew_mean     | 122      |\n","| time/              |          |\n","|    episodes        | 12       |\n","|    fps             | 112      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 293      |\n","| train/             |          |\n","|    actor_loss      | -30.1    |\n","|    critic_loss     | 10.3     |\n","|    ent_coef        | 0.94     |\n","|    ent_coef_loss   | -1.74    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 192      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 23.6     |\n","|    ep_rew_mean     | 118      |\n","| time/              |          |\n","|    episodes        | 16       |\n","|    fps             | 101      |\n","|    time_elapsed    | 3        |\n","|    total_timesteps | 378      |\n","| train/             |          |\n","|    actor_loss      | -33.2    |\n","|    critic_loss     | 8.11     |\n","|    ent_coef        | 0.915    |\n","|    ent_coef_loss   | -2.49    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 277      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 22.8     |\n","|    ep_rew_mean     | 113      |\n","| time/              |          |\n","|    episodes        | 20       |\n","|    fps             | 95       |\n","|    time_elapsed    | 4        |\n","|    total_timesteps | 455      |\n","| train/             |          |\n","|    actor_loss      | -37.2    |\n","|    critic_loss     | 6.93     |\n","|    ent_coef        | 0.893    |\n","|    ent_coef_loss   | -3.19    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 354      |\n","---------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 26.8     |\n","|    ep_rew_mean     | 134      |\n","| time/              |          |\n","|    episodes        | 4        |\n","|    fps             | 1278     |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 107      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 27.1     |\n","|    ep_rew_mean     | 129      |\n","| time/              |          |\n","|    episodes        | 8        |\n","|    fps             | 249      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 217      |\n","| train/             |          |\n","|    actor_loss      | -6.02    |\n","|    critic_loss     | 0.976    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 106      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 26.3     |\n","|    ep_rew_mean     | 121      |\n","| time/              |          |\n","|    episodes        | 12       |\n","|    fps             | 194      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 316      |\n","| train/             |          |\n","|    actor_loss      | -7.38    |\n","|    critic_loss     | 0.715    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 211      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 25.8     |\n","|    ep_rew_mean     | 117      |\n","| time/              |          |\n","|    episodes        | 16       |\n","|    fps             | 176      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 413      |\n","| train/             |          |\n","|    actor_loss      | -8.36    |\n","|    critic_loss     | 0.691    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 309      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 25.9     |\n","|    ep_rew_mean     | 115      |\n","| time/              |          |\n","|    episodes        | 20       |\n","|    fps             | 166      |\n","|    time_elapsed    | 3        |\n","|    total_timesteps | 518      |\n","| train/             |          |\n","|    actor_loss      | -9.25    |\n","|    critic_loss     | 1.08     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 411      |\n","---------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","------------------------------------\n","| rollout/              |          |\n","|    ep_len_mean        | 24.4     |\n","|    ep_rew_mean        | 122      |\n","| time/                 |          |\n","|    fps                | 371      |\n","|    iterations         | 100      |\n","|    time_elapsed       | 1        |\n","|    total_timesteps    | 500      |\n","| train/                |          |\n","|    entropy_loss       | -23.9    |\n","|    explained_variance | -0.0112  |\n","|    learning_rate      | 0.0007   |\n","|    n_updates          | 99       |\n","|    policy_loss        | 312      |\n","|    std                | 0.987    |\n","|    value_loss         | 196      |\n","------------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 21.5     |\n","|    ep_rew_mean     | 106      |\n","| time/              |          |\n","|    fps             | 476      |\n","|    iterations      | 1        |\n","|    time_elapsed    | 4        |\n","|    total_timesteps | 2048     |\n","---------------------------------\n","85.0 %\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 27.2     |\n","|    ep_rew_mean     | 138      |\n","| time/              |          |\n","|    episodes        | 4        |\n","|    fps             | 352      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 109      |\n","| train/             |          |\n","|    actor_loss      | 15.3     |\n","|    critic_loss     | 495      |\n","|    ent_coef        | 1        |\n","|    ent_coef_loss   | 0.0156   |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 8        |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 27.6     |\n","|    ep_rew_mean     | 139      |\n","| time/              |          |\n","|    episodes        | 8        |\n","|    fps             | 99       |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 221      |\n","| train/             |          |\n","|    actor_loss      | -28.5    |\n","|    critic_loss     | 14       |\n","|    ent_coef        | 0.965    |\n","|    ent_coef_loss   | -1       |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 120      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 25.2     |\n","|    ep_rew_mean     | 126      |\n","| time/              |          |\n","|    episodes        | 12       |\n","|    fps             | 84       |\n","|    time_elapsed    | 3        |\n","|    total_timesteps | 303      |\n","| train/             |          |\n","|    actor_loss      | -32.2    |\n","|    critic_loss     | 8.48     |\n","|    ent_coef        | 0.94     |\n","|    ent_coef_loss   | -1.76    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 202      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 23.9     |\n","|    ep_rew_mean     | 118      |\n","| time/              |          |\n","|    episodes        | 16       |\n","|    fps             | 82       |\n","|    time_elapsed    | 4        |\n","|    total_timesteps | 382      |\n","| train/             |          |\n","|    actor_loss      | -34.8    |\n","|    critic_loss     | 9.46     |\n","|    ent_coef        | 0.917    |\n","|    ent_coef_loss   | -2.46    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 281      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 24.4     |\n","|    ep_rew_mean     | 120      |\n","| time/              |          |\n","|    episodes        | 20       |\n","|    fps             | 81       |\n","|    time_elapsed    | 5        |\n","|    total_timesteps | 487      |\n","| train/             |          |\n","|    actor_loss      | -39.2    |\n","|    critic_loss     | 6.13     |\n","|    ent_coef        | 0.887    |\n","|    ent_coef_loss   | -3.37    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 386      |\n","---------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 28.8     |\n","|    ep_rew_mean     | 155      |\n","| time/              |          |\n","|    episodes        | 4        |\n","|    fps             | 721      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 115      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 20.9     |\n","|    ep_rew_mean     | 114      |\n","| time/              |          |\n","|    episodes        | 8        |\n","|    fps             | 244      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 167      |\n","| train/             |          |\n","|    actor_loss      | -5.89    |\n","|    critic_loss     | 3.6      |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 55       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 18.4     |\n","|    ep_rew_mean     | 99.7     |\n","| time/              |          |\n","|    episodes        | 12       |\n","|    fps             | 186      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 221      |\n","| train/             |          |\n","|    actor_loss      | -7.03    |\n","|    critic_loss     | 2.43     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 108      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 17.1     |\n","|    ep_rew_mean     | 93       |\n","| time/              |          |\n","|    episodes        | 16       |\n","|    fps             | 159      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 274      |\n","| train/             |          |\n","|    actor_loss      | -7.58    |\n","|    critic_loss     | 1.19     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 161      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 16.4     |\n","|    ep_rew_mean     | 89       |\n","| time/              |          |\n","|    episodes        | 20       |\n","|    fps             | 145      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 327      |\n","| train/             |          |\n","|    actor_loss      | -8.1     |\n","|    critic_loss     | 0.743    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 214      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 15.8     |\n","|    ep_rew_mean     | 86       |\n","| time/              |          |\n","|    episodes        | 24       |\n","|    fps             | 137      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 379      |\n","| train/             |          |\n","|    actor_loss      | -8.94    |\n","|    critic_loss     | 0.475    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 267      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 15.4     |\n","|    ep_rew_mean     | 84       |\n","| time/              |          |\n","|    episodes        | 28       |\n","|    fps             | 131      |\n","|    time_elapsed    | 3        |\n","|    total_timesteps | 431      |\n","| train/             |          |\n","|    actor_loss      | -9.32    |\n","|    critic_loss     | 0.632    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 319      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 15.1     |\n","|    ep_rew_mean     | 82.5     |\n","| time/              |          |\n","|    episodes        | 32       |\n","|    fps             | 130      |\n","|    time_elapsed    | 3        |\n","|    total_timesteps | 484      |\n","| train/             |          |\n","|    actor_loss      | -10      |\n","|    critic_loss     | 0.476    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 372      |\n","---------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","------------------------------------\n","| rollout/              |          |\n","|    ep_len_mean        | 20.1     |\n","|    ep_rew_mean        | 98.2     |\n","| time/                 |          |\n","|    fps                | 366      |\n","|    iterations         | 100      |\n","|    time_elapsed       | 1        |\n","|    total_timesteps    | 500      |\n","| train/                |          |\n","|    entropy_loss       | -24      |\n","|    explained_variance | 0.00449  |\n","|    learning_rate      | 0.0007   |\n","|    n_updates          | 99       |\n","|    policy_loss        | 620      |\n","|    std                | 0.995    |\n","|    value_loss         | 735      |\n","------------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 21.5     |\n","|    ep_rew_mean     | 105      |\n","| time/              |          |\n","|    fps             | 417      |\n","|    iterations      | 1        |\n","|    time_elapsed    | 4        |\n","|    total_timesteps | 2048     |\n","---------------------------------\n","90.0 %\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 25.5     |\n","|    ep_rew_mean     | 126      |\n","| time/              |          |\n","|    episodes        | 4        |\n","|    fps             | 884      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 102      |\n","| train/             |          |\n","|    actor_loss      | 35.3     |\n","|    critic_loss     | 2.87e+03 |\n","|    ent_coef        | 1        |\n","|    ent_coef_loss   | 0        |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 1        |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 22.6     |\n","|    ep_rew_mean     | 110      |\n","| time/              |          |\n","|    episodes        | 8        |\n","|    fps             | 150      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 181      |\n","| train/             |          |\n","|    actor_loss      | -25.2    |\n","|    critic_loss     | 20.4     |\n","|    ent_coef        | 0.978    |\n","|    ent_coef_loss   | -0.617   |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 80       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 25.1     |\n","|    ep_rew_mean     | 128      |\n","| time/              |          |\n","|    episodes        | 12       |\n","|    fps             | 96       |\n","|    time_elapsed    | 3        |\n","|    total_timesteps | 301      |\n","| train/             |          |\n","|    actor_loss      | -31.8    |\n","|    critic_loss     | 8.33     |\n","|    ent_coef        | 0.941    |\n","|    ent_coef_loss   | -1.71    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 200      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 25.6     |\n","|    ep_rew_mean     | 131      |\n","| time/              |          |\n","|    episodes        | 16       |\n","|    fps             | 81       |\n","|    time_elapsed    | 5        |\n","|    total_timesteps | 409      |\n","| train/             |          |\n","|    actor_loss      | -35.6    |\n","|    critic_loss     | 7.5      |\n","|    ent_coef        | 0.91     |\n","|    ent_coef_loss   | -2.67    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 308      |\n","---------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 21.5     |\n","|    ep_rew_mean     | 107      |\n","| time/              |          |\n","|    episodes        | 4        |\n","|    fps             | 1293     |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 86       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 20       |\n","|    ep_rew_mean     | 98.3     |\n","| time/              |          |\n","|    episodes        | 8        |\n","|    fps             | 315      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 160      |\n","| train/             |          |\n","|    actor_loss      | -6.51    |\n","|    critic_loss     | 9.02     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 59       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 18.9     |\n","|    ep_rew_mean     | 91.7     |\n","| time/              |          |\n","|    episodes        | 12       |\n","|    fps             | 234      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 227      |\n","| train/             |          |\n","|    actor_loss      | -7.67    |\n","|    critic_loss     | 1.54     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 125      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 18.1     |\n","|    ep_rew_mean     | 87.8     |\n","| time/              |          |\n","|    episodes        | 16       |\n","|    fps             | 205      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 289      |\n","| train/             |          |\n","|    actor_loss      | -8.64    |\n","|    critic_loss     | 1.03     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 187      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 17.4     |\n","|    ep_rew_mean     | 86.4     |\n","| time/              |          |\n","|    episodes        | 20       |\n","|    fps             | 187      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 349      |\n","| train/             |          |\n","|    actor_loss      | -9.68    |\n","|    critic_loss     | 1.1      |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 248      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 17       |\n","|    ep_rew_mean     | 85.6     |\n","| time/              |          |\n","|    episodes        | 24       |\n","|    fps             | 179      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 407      |\n","| train/             |          |\n","|    actor_loss      | -10.5    |\n","|    critic_loss     | 1.95     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 307      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 16.6     |\n","|    ep_rew_mean     | 84.8     |\n","| time/              |          |\n","|    episodes        | 28       |\n","|    fps             | 173      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 465      |\n","| train/             |          |\n","|    actor_loss      | -11.3    |\n","|    critic_loss     | 2.52     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 364      |\n","---------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","------------------------------------\n","| rollout/              |          |\n","|    ep_len_mean        | 20.7     |\n","|    ep_rew_mean        | 99.7     |\n","| time/                 |          |\n","|    fps                | 366      |\n","|    iterations         | 100      |\n","|    time_elapsed       | 1        |\n","|    total_timesteps    | 500      |\n","| train/                |          |\n","|    entropy_loss       | -24.1    |\n","|    explained_variance | -0.119   |\n","|    learning_rate      | 0.0007   |\n","|    n_updates          | 99       |\n","|    policy_loss        | 316      |\n","|    std                | 0.998    |\n","|    value_loss         | 204      |\n","------------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 21.5     |\n","|    ep_rew_mean     | 106      |\n","| time/              |          |\n","|    fps             | 536      |\n","|    iterations      | 1        |\n","|    time_elapsed    | 3        |\n","|    total_timesteps | 2048     |\n","---------------------------------\n","95.0 %\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 22.2     |\n","|    ep_rew_mean     | 112      |\n","| time/              |          |\n","|    episodes        | 4        |\n","|    fps             | 705      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 89       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 22.1     |\n","|    ep_rew_mean     | 112      |\n","| time/              |          |\n","|    episodes        | 8        |\n","|    fps             | 122      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 177      |\n","| train/             |          |\n","|    actor_loss      | -24.5    |\n","|    critic_loss     | 20.9     |\n","|    ent_coef        | 0.977    |\n","|    ent_coef_loss   | -0.621   |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 76       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 22.8     |\n","|    ep_rew_mean     | 113      |\n","| time/              |          |\n","|    episodes        | 12       |\n","|    fps             | 92       |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 273      |\n","| train/             |          |\n","|    actor_loss      | -29.8    |\n","|    critic_loss     | 16.4     |\n","|    ent_coef        | 0.948    |\n","|    ent_coef_loss   | -1.51    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 172      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 22.1     |\n","|    ep_rew_mean     | 109      |\n","| time/              |          |\n","|    episodes        | 16       |\n","|    fps             | 86       |\n","|    time_elapsed    | 4        |\n","|    total_timesteps | 353      |\n","| train/             |          |\n","|    actor_loss      | -32.9    |\n","|    critic_loss     | 9.91     |\n","|    ent_coef        | 0.924    |\n","|    ent_coef_loss   | -2.22    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 252      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 21.9     |\n","|    ep_rew_mean     | 108      |\n","| time/              |          |\n","|    episodes        | 20       |\n","|    fps             | 84       |\n","|    time_elapsed    | 5        |\n","|    total_timesteps | 439      |\n","| train/             |          |\n","|    actor_loss      | -36.9    |\n","|    critic_loss     | 7.65     |\n","|    ent_coef        | 0.9      |\n","|    ent_coef_loss   | -2.98    |\n","|    learning_rate   | 0.0003   |\n","|    n_updates       | 338      |\n","---------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 25.2     |\n","|    ep_rew_mean     | 126      |\n","| time/              |          |\n","|    episodes        | 4        |\n","|    fps             | 1185     |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 101      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 19.5     |\n","|    ep_rew_mean     | 102      |\n","| time/              |          |\n","|    episodes        | 8        |\n","|    fps             | 309      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 156      |\n","| train/             |          |\n","|    actor_loss      | -4.68    |\n","|    critic_loss     | 4.78     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 58       |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 17.6     |\n","|    ep_rew_mean     | 93.6     |\n","| time/              |          |\n","|    episodes        | 12       |\n","|    fps             | 230      |\n","|    time_elapsed    | 0        |\n","|    total_timesteps | 211      |\n","| train/             |          |\n","|    actor_loss      | -5.62    |\n","|    critic_loss     | 1.62     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 113      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 16.6     |\n","|    ep_rew_mean     | 89.6     |\n","| time/              |          |\n","|    episodes        | 16       |\n","|    fps             | 201      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 266      |\n","| train/             |          |\n","|    actor_loss      | -6.45    |\n","|    critic_loss     | 2.14     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 168      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 16.1     |\n","|    ep_rew_mean     | 86.9     |\n","| time/              |          |\n","|    episodes        | 20       |\n","|    fps             | 187      |\n","|    time_elapsed    | 1        |\n","|    total_timesteps | 321      |\n","| train/             |          |\n","|    actor_loss      | -7.43    |\n","|    critic_loss     | 0.789    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 223      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 15.7     |\n","|    ep_rew_mean     | 85.4     |\n","| time/              |          |\n","|    episodes        | 24       |\n","|    fps             | 176      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 376      |\n","| train/             |          |\n","|    actor_loss      | -8.41    |\n","|    critic_loss     | 0.995    |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 278      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 15.4     |\n","|    ep_rew_mean     | 84.2     |\n","| time/              |          |\n","|    episodes        | 28       |\n","|    fps             | 160      |\n","|    time_elapsed    | 2        |\n","|    total_timesteps | 431      |\n","| train/             |          |\n","|    actor_loss      | -9.04    |\n","|    critic_loss     | 1.16     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 334      |\n","---------------------------------\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 15.2     |\n","|    ep_rew_mean     | 83.6     |\n","| time/              |          |\n","|    episodes        | 32       |\n","|    fps             | 150      |\n","|    time_elapsed    | 3        |\n","|    total_timesteps | 487      |\n","| train/             |          |\n","|    actor_loss      | -9.78    |\n","|    critic_loss     | 1.09     |\n","|    learning_rate   | 0.001    |\n","|    n_updates       | 389      |\n","---------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","------------------------------------\n","| rollout/              |          |\n","|    ep_len_mean        | 21.3     |\n","|    ep_rew_mean        | 105      |\n","| time/                 |          |\n","|    fps                | 376      |\n","|    iterations         | 100      |\n","|    time_elapsed       | 1        |\n","|    total_timesteps    | 500      |\n","| train/                |          |\n","|    entropy_loss       | -24      |\n","|    explained_variance | 0.0653   |\n","|    learning_rate      | 0.0007   |\n","|    n_updates          | 99       |\n","|    policy_loss        | 312      |\n","|    std                | 0.991    |\n","|    value_loss         | 196      |\n","------------------------------------\n","Using cuda device\n","Wrapping the env with a `Monitor` wrapper\n","Wrapping the env in a DummyVecEnv.\n","---------------------------------\n","| rollout/           |          |\n","|    ep_len_mean     | 22.2     |\n","|    ep_rew_mean     | 110      |\n","| time/              |          |\n","|    fps             | 425      |\n","|    iterations      | 1        |\n","|    time_elapsed    | 4        |\n","|    total_timesteps | 2048     |\n","---------------------------------\n"]}]},{"cell_type":"code","source":["print(avg_ep_rews)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pv1KVvH8Ven8","executionInfo":{"status":"ok","timestamp":1702289185694,"user_tz":-60,"elapsed":13,"user":{"displayName":"Alberto Saiz","userId":"03975085568355897980"}},"outputId":"a69663d6-9f5b-48e6-80e1-fd22b11e3eb7"},"execution_count":28,"outputs":[{"output_type":"stream","name":"stdout","text":["[[101.50493897 125.70797594 111.46835714  94.8187329  103.44319762\n","  101.121044    96.33626863 109.44759265 100.08116246  86.26725671\n","  107.59536678 101.68366842 128.46388015 109.46161897 102.14164823\n","  100.8711969  106.42791263 113.96661416 126.91538871  97.82861398]\n"," [ 92.41451007 108.00550064  60.28034191  60.90066806  75.12087137\n","   67.88829887  63.6396437  128.46824226 300.72601469  70.5114792\n","   86.3500975   96.6970665   71.90375741  87.925665    86.69814335\n","   91.12317965 106.97043596  73.04681399  82.02295072  76.69569575]\n"," [106.12951308 112.40756013 102.28399355 102.60585383 106.71562475\n","  109.26148899 108.51448229 110.91058832 113.74182099 107.33523779\n","  104.69040687 107.60073103 101.33104668 107.69036525 108.58671294\n","  101.13097512 112.40068211 103.4232412  104.92425814 114.10971249]\n"," [114.2987488  114.85653027 112.38643211 112.86677827 113.57408974\n","  109.89971586 110.47240726 111.05698912 114.93576606 109.31426345\n","  110.60245834 109.8767349  111.14021702 110.99110812 110.30077061\n","  109.5843447  112.13591002 109.15089171 111.23161718 111.85801464]]\n"]}]},{"cell_type":"markdown","source":["# Plot of results"],"metadata":{"id":"HKV_g6R9QaG8"}},{"cell_type":"code","source":["plt.figure(figsize=(10,5))\n","plt.grid(True)\n","color_alg=[\"r\", \"g\", \"b\", \"k\"]\n","for i in range(4):\n","  '''\n","  plt.subplot(1,2,1)\n","  plt.grid(True)\n","  plt.title(\"Average Episode Length\")\n","  plt.xlabel(\"Episode\")\n","  plt.ylabel(\"Episode Length\")\n","  plt.plot(range(NUM_ROUNDS),avg_ep_lens[i], marker=\"*\", color=color_alg[i],linestyle=\"--\", label=algorithms[i])\n","  plt.grid()\n","  plt.legend()\n","  plt.subplot(1,2,2)\n","  '''\n","  plt.title(\"Average Episode Reward\")\n","  plt.xlabel(\"Episode\")\n","  plt.ylabel(\"Reward\")\n","  plt.plot(range(NUM_ROUNDS),avg_ep_rews[i], marker=\"*\",color=color_alg[i],linestyle=\"-\",label=algorithms[i])\n","  plt.legend()\n","  #plt.ylim(-300, 300)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":487},"id":"XCWCntavOprv","executionInfo":{"status":"ok","timestamp":1702291790626,"user_tz":-60,"elapsed":806,"user":{"displayName":"Alberto Saiz","userId":"03975085568355897980"}},"outputId":"70fdcb2e-347c-4c82-de20-0355315c0070"},"execution_count":59,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1000x500 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":[],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pO_3LIz1gWdA","executionInfo":{"status":"ok","timestamp":1702289186261,"user_tz":-60,"elapsed":12,"user":{"displayName":"Alberto Saiz","userId":"03975085568355897980"}},"outputId":"3b71ee2b-c048-444b-dfb1-3ca80092df21"},"execution_count":31,"outputs":[{"output_type":"stream","name":"stdout","text":["╒═══════╤═══════════════╤══════════════════╕\n","│ Alg   │   Mean Reward │   Mean Ep Length │\n","╞═══════╪═══════════════╪══════════════════╡\n","│ SAC   │       186.278 │          21.3907 │\n","├───────┼───────────────┼──────────────────┤\n","│ TD3   │       174.369 │          17.1397 │\n","├───────┼───────────────┼──────────────────┤\n","│ A2C   │       187.29  │          21.8597 │\n","├───────┼───────────────┼──────────────────┤\n","│ PPO   │       191.527 │          22.562  │\n","╘═══════╧═══════════════╧══════════════════╛\n"]}]},{"cell_type":"code","source":["avg_increase=[]\n","for i in range(4):\n","  aux=[]\n","  for j in range(1, NUM_ROUNDS):\n","    aux.append(avg_ep_rews[i][j]-avg_ep_rews[i][j-1])\n","  avg_increase.append(np.mean(aux))"],"metadata":{"id":"lXZSGPk5deeA","executionInfo":{"status":"ok","timestamp":1702291594701,"user_tz":-60,"elapsed":250,"user":{"displayName":"Alberto Saiz","userId":"03975085568355897980"}}},"execution_count":54,"outputs":[]},{"cell_type":"code","source":["regress_slope=[]\n","regress_inter=[]\n","for i in range(4):\n","  slope, intercept = np.polyfit(range(len(avg_ep_rews[i])), avg_ep_rews[i], 1)\n","  regress_slope.append(slope)\n","  regress_inter.append(intercept)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"NCDkUJzMfLV3","executionInfo":{"status":"ok","timestamp":1702291780421,"user_tz":-60,"elapsed":327,"user":{"displayName":"Alberto Saiz","userId":"03975085568355897980"}},"outputId":"b0b05ac6-6880-4b8e-b2c9-e3c2cf971732"},"execution_count":58,"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n","  and should_run_async(code)\n"]}]},{"cell_type":"code","source":["from tabulate import tabulate\n","mean_avg_ep_rews=np.mean(avg_ep_rews, axis=1)\n","mean_avg_ep_lens=np.mean(avg_ep_lens, axis=1)\n","mean_avg_norm_ep_rews=np.mean(avg_ep_rews, axis=1)\n","# Crear una lista de listas para los datos de la tabla\n","datos_tabla = list(zip(algorithms, mean_avg_norm_ep_rews, mean_avg_ep_lens, regress_slope))\n","\n","# Encabezados de la tabla\n","encabezados = ['Alg', 'Mean Reward', 'Mean Ep Length', \"Regression Line Slope\"]\n","\n","# Imprimir la tabla\n","tabla = tabulate(datos_tabla, headers=encabezados, tablefmt='fancy_grid')\n","\n","print(tabla)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"tg3s42OOfbl2","executionInfo":{"status":"ok","timestamp":1702291973950,"user_tz":-60,"elapsed":369,"user":{"displayName":"Alberto Saiz","userId":"03975085568355897980"}},"outputId":"d174dedd-8e3f-4fa7-8917-263ec20020a9"},"execution_count":67,"outputs":[{"output_type":"stream","name":"stdout","text":["╒═══════╤═══════════════╤══════════════════╤═════════════════════════╕\n","│ Alg   │   Mean Reward │   Mean Ep Length │   Regression Line Slope │\n","╞═══════╪═══════════════╪══════════════════╪═════════════════════════╡\n","│ SAC   │      106.278  │          21.3907 │               0.250419  │\n","├───────┼───────────────┼──────────────────┼─────────────────────────┤\n","│ TD3   │       94.3695 │          17.1397 │              -0.235873  │\n","├───────┼───────────────┼──────────────────┼─────────────────────────┤\n","│ A2C   │      107.29   │          21.8597 │               0.0199928 │\n","├───────┼───────────────┼──────────────────┼─────────────────────────┤\n","│ PPO   │      111.527  │          22.562  │              -0.16252   │\n","╘═══════╧═══════════════╧══════════════════╧═════════════════════════╛\n"]}]},{"cell_type":"markdown","source":["Zoomed slope"],"metadata":{"id":"3RBXtjizfq3d"}},{"cell_type":"code","source":["plt.figure(figsize=(15,10))\n","for i in  [0,2,3]:\n","  plt.title(\"Average Episode Reward\")\n","  plt.xlabel(\"Episode\")\n","  plt.ylabel(\"Reward\")\n","  plt.plot(range(NUM_ROUNDS),avg_ep_rews[i], marker=\"*\",color=color_alg[i],linestyle=\"-\",label=algorithms[i])\n","  x=np.linspace(0, NUM_ROUNDS, 5000)\n","  plt.plot(x, regress_slope[i]*x+regress_inter[i], color=color_alg[i], linestyle=\"--\", alpha=0.6, label=f\"Regression {algorithms[i]}\")\n","  plt.legend()\n","  plt.grid()\n","  #plt.ylim(-300, 30)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":872},"id":"soS4t0KHfqAe","executionInfo":{"status":"ok","timestamp":1702291936527,"user_tz":-60,"elapsed":1566,"user":{"displayName":"Alberto Saiz","userId":"03975085568355897980"}},"outputId":"5b1549e7-fe0e-4e53-bbea-1ac866f20079"},"execution_count":66,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 1500x1000 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]}]}