A2C PPO.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Actor Critic with PPO\n",
    "\n",
    "There are two main portions to this notebook \n",
    "- Gathering trajectories\n",
    "- Learning from the trajectories\n",
    "\n",
    "Notebook designed for readability and exploration rather than production. Uses a single GPU. For an industrial-strength PPO in PyTorch check out [ikostrikov's](https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail). For the 'definitive' implementation of PPO, check out [OpenAI baselines](https://github.com/openai/baselines/tree/master/baselines/ppo2) (tensorflow).\n",
    "\n",
    "Experiments below reproduce results from OpenAI's [procedually-generated environments](https://openai.com/blog/procgen-benchmark/). All hyperparameters taken directly from the [procgen paper](https://arxiv.org/abs/1912.01588). Everything built from scratch unless otherwise noted in effort to gain intuition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 67
    },
    "colab_type": "code",
    "id": "AoHncnzFE3sh",
    "outputId": "24a62a39-5e67-4c67-e4c3-ab02e7368420",
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import gym\n",
    "import random\n",
    "import cv2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cuda\n"
     ]
    }
   ],
   "source": [
    "# Check for cuda. Can run on CPU if necessary. \n",
    "\n",
    "device = 'cuda' if torch.cuda.is_available else 'cpu'; print(device)\n",
    "if device != 'cuda': print(\"No cuda detected. Consider colab.research.google.com for free GPUs.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "K4Ju-bB2FLFn"
   },
   "outputs": [],
   "source": [
    "# If no local GPU or want to run multiple notebooks simultaneously, use Google Colab. In that case, have to install procgen\n",
    "\n",
    "try: \n",
    "    from procgen import ProcgenEnv\n",
    "except: \n",
    "    !pip install procgen\n",
    "    from procgen import ProcgenEnv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 236,
     "resources": {
      "http://localhost:8080/nbextensions/google.colab/files.js": {
       "data": 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       "ok": true,
       "status": 200,
       "status_text": ""
      }
     }
    },
    "colab_type": "code",
    "id": "-9TG0rO2GTBH",
    "outputId": "76380af1-1abf-4609-a347-dacd03df5578"
   },
   "outputs": [],
   "source": [
    "# Same thing, for colab.\n",
    "\n",
    "try:\n",
    "    from utils import *\n",
    "    from models import *\n",
    "except:\n",
    "    from google.colab import files\n",
    "    src = list(files.upload().values())[0] # grab files from local and upload to colab\n",
    "    from utils import *\n",
    "    from models import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Agent\n",
    "Using \"Impala CNN\" as referenced in procgen paper. Architecture from [the original IMPALA paper](https://arxiv.org/abs/1802.01561). Basically just a small vanilla resnet (0.3M params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ACApmQdsE3sw"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.308 M params'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agent = ImpalaCNN().to(device) # see models.py for architecture\n",
    "get_n_params(agent)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Environment\n",
    "\n",
    "There are 16 procedurally-generated environments to choose from. Goal is to encourage and test ability to generalize rather than overfit to specific game. \n",
    "\n",
    "Using [vectorized environments](https://stable-baselines.readthedocs.io/en/master/guide/vec_envs.html) from OpenAI. Vecenvs are normal envs stacked side by side. They progress \"shoulder to shoulder\", giving us more variety in our rollouts than a single env could. To change environment simply swap out env_name. Nothing else changes. Currently set to 'easy' setting but also replicated results on 'hard'. To train on 'hard', increase training steps from 25M to 200M and do gradient accumulation of 4 steps to mimic four workers (see below). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vCpcHwaCE3s5"
   },
   "outputs": [],
   "source": [
    "n_envs = 72 # Procgen paper uses 64 but that's the same shape as img size. Prefer unique dims to faciliate debugging.\n",
    "n_levels = 0 # Zero for all levels\n",
    "env_name = 'starpilot' \n",
    "\n",
    "env = ProcgenEnv(num_envs=n_envs, env_name=env_name, start_level=0, num_levels=n_levels, distribution_mode='easy')\n",
    "env = VecExtractDictObs(env, \"rgb\") \n",
    "env = VecMonitor(env)\n",
    "env = VecNormalize(env, ob=False) # normalizing returns, but not the img frames.\n",
    "#env = VecFrameStack(env, nstack=4) # procgen paper found limited help from framestacking"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Load trained model if you have one\n",
    "\n",
    "load_pretrained = False\n",
    "\n",
    "if load_pretrained:\n",
    "    try:\n",
    "        agent.load_state_dict(torch.load(env_name+\" agent.torch\"), strict=False)\n",
    "        print(\"Successfully loaded saved model!\")\n",
    "    except:\n",
    "        print(\"No model saved yet\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 151
    },
    "colab_type": "code",
    "id": "L3FX9JPQE3s9",
    "outputId": "f676c6da-8f76-4ccc-f048-492491f22753",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(72, 64, 64, 3)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# take random first step in our envs\n",
    "\n",
    "env.reset()\n",
    "frame, reward, done, info = env.step(np.array([env.action_space.sample() for _ in range(n_envs)])); \n",
    "frame.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 285
    },
    "colab_type": "code",
    "id": "C4msy7YpE3tE",
    "outputId": "c6171666-9f2f-4eda-9bb9-a6d9d7bc2a16"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x23918f71808>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(frame[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "w5pCYBzsE3tH"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([72, 3, 64, 64])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame = np_to_pytorch_img(frame).to(device)\n",
    "frame.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "_o8hdtGyE3tN",
    "outputId": "14837f6d-5746-431a-9f6b-f1f28903b976",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([72, 15]), torch.Size([72, 1]))"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test out agent and env\n",
    "\n",
    "with torch.no_grad():\n",
    "    action_log_probs, state_estimate = agent(frame); \n",
    "\n",
    "action_log_probs.shape, state_estimate.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 84
    },
    "colab_type": "code",
    "id": "1ijv2mhpE3tY",
    "outputId": "509224a1-6c44-4828-c00a-ab41a1602183"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([ 6,  4, 12,  7,  8, 12,  5,  3, 12, 12,  4, 12,  4,  5,  2, 10,  3,  9,\n",
       "        13,  7,  4,  0,  6,  4, 12, 12,  2, 13, 11, 11,  5,  5,  1, 13,  6,  2,\n",
       "        12, 14,  2, 11, 11,  1, 11,  2,  3, 13,  9,  5, 12,  6, 14,  1,  3,  8,\n",
       "         0, 11, 10,  8,  3,  5, 12,  6,  3,  1,  1,  1,  7,  4, 11, 11, 10,  4],\n",
       "       device='cuda:0')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sample an action from the action probs distribution.\n",
    "\n",
    "get_action(action_log_probs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1K7GWtJuE3te"
   },
   "source": [
    "## Calculate Returns\n",
    "\n",
    "Using [Generalized Advantage Estimation (GAE)](https://arxiv.org/abs/1506.02438). Also have vanilla discounted returns for comparison"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "1LS-zWKXE3tf",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Manually create a mock dataset of rewards to inspect our returns calculators\n",
    "\n",
    "rewards = np.zeros((32, 5)) # shape mimics 5 envs taking 32 steps. This is like how we'll get it from vecenvs\n",
    "\n",
    "rewards[10, 1] = 3\n",
    "rewards[5, 3] = 4\n",
    "rewards[20, 0] = 5\n",
    "rewards[31, 0] = 3.66\n",
    "\n",
    "dones = np.zeros_like(rewards)\n",
    "dones[5, 1] = 1\n",
    "dones[15, 3] = 1\n",
    "dones[20, 0] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 554
    },
    "colab_type": "code",
    "id": "XAvfCgNiE3tk",
    "outputId": "996d705f-da61-49ba-d23b-bd57674c9345",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def compute_returns(rewards, dones):\n",
    "    \"\"\" Vanilla method of computing discounted returns. Does not use GAE \"\"\"\n",
    "    \n",
    "    GAMMA = .95\n",
    "    R = []\n",
    "    r2 = rewards[-1] # this is ~v(s)\n",
    "    R.append(r2)\n",
    "    for i in reversed(range(0, len(rewards)-1)):\n",
    "        reward = rewards[i]\n",
    "        r1_is_terminal = (dones[i]-1)*-1 # so Dones are zeros\n",
    "        r1 = reward + ((r2 * GAMMA)*r1_is_terminal) # If r1 is terminal, don't add in the discounted returns of next state\n",
    "        R.append(r1)\n",
    "        r2=r1\n",
    "        \n",
    "    R.reverse()\n",
    "    R = np.array(R)\n",
    "    return R\n",
    "\n",
    "# Testing \n",
    "vanilla_returns = compute_returns(rewards, dones)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Vanilla Returns')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Using simple discounted returns, here's how our rewards translate into returns.\n",
    "\n",
    "plt.plot(range(len(vanilla_returns)), [_[0] for _ in vanilla_returns])\n",
    "plt.title(\"Vanilla Returns\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "oAptYy-fE3tm",
    "outputId": "df19f90b-0a10-4e7f-c44c-164efa7b646d",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def compute_returns_gae(rewards, dones, values):\n",
    "    \"\"\" Compute discounted returns w Generalized Advantage Estimation (GAE) \"\"\"\n",
    "    \n",
    "    GAMMA = .999\n",
    "    LAM = .95\n",
    "    lastgaelam = 0\n",
    "    A = [np.zeros(rewards.shape[-1])]\n",
    "    for t in reversed(range(0, len(rewards)-1)):\n",
    "        nextnonterminal = 1.0 - dones[t] # openai baselines records dones in different way. Customized this. Was t+1\n",
    "        nextvalues = values[t+1]\n",
    "        delta = rewards[t] + GAMMA * nextvalues * nextnonterminal - values[t]\n",
    "        lastgaelam = delta + GAMMA * LAM * nextnonterminal * lastgaelam\n",
    "        A.append(lastgaelam)\n",
    "    A.reverse()\n",
    "    A = np.array(A)\n",
    "    R = A + values\n",
    "    return R\n",
    "\n",
    "# Using vanilla returns in place of estimated state values. \n",
    "gae_returns = compute_returns_gae(rewards, dones, vanilla_returns);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'GAE Returns')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Test GAE returns w same rewards as above. The difference btwn vanilla and GAE returns won't always look like this. \n",
    "\n",
    "plt.plot(range(len(gae_returns)), [_[0] for _ in gae_returns])\n",
    "plt.title(\"GAE Returns\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Gather Trajectories "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "DuEQQS0oE3tn"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(72, 64, 64, 3)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize w first frames\n",
    "\n",
    "last_frame_ = env.reset(); last_frame_.shape #(n_envs, height, width, channels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iN5-48wIE3tq"
   },
   "outputs": [],
   "source": [
    "# Gather trajectories for n_steps. Vecenvs call reset at the end of a game so we can just keep playing and playing...\n",
    "\n",
    "n_steps = 256\n",
    "\n",
    "def gather_trajectories():\n",
    "    with torch.no_grad():\n",
    "        agent.eval()\n",
    "        global last_frame_\n",
    "        frame = last_frame_\n",
    "        frames_, rewards_, dones_, actions_, old_action_probs_, old_state_estimates_, epinfos = [], [], [], [], [], [], []\n",
    "        \n",
    "        for s in range(n_steps):\n",
    "            frames_.append(frame);\n",
    "            action_probs, state_estimate = agent(np_to_pytorch_img(frame).to(device));\n",
    "            action = get_action(action_probs).cpu().numpy();\n",
    "            frame, reward, done, info = env.step(action)\n",
    "            rewards_.append(reward); dones_.append(done); actions_.append(action); old_action_probs_.append(action_probs.detach().cpu().numpy()); old_state_estimates_.append(state_estimate.detach().cpu().numpy())\n",
    "\n",
    "            for i in info:\n",
    "                episode_info = i.get('episode')\n",
    "                if episode_info: epinfos.append(episode_info)\n",
    "\n",
    "        rewards_ = np.array(rewards_); dones_ = np.array(dones_); actions_ = np.array(actions_); frames_ = np.array(frames_); old_state_estimates_ = np.array(old_state_estimates_);\n",
    "        \n",
    "        last_frame_ = frames_[-1] # reset global last frame. Next time we gather trajectories we'll pick up here\n",
    "        \n",
    "        rewards_[-1] = state_estimate.squeeze(-1).cpu().numpy() # Bootstrapped returns\n",
    "\n",
    "        returns_ = compute_returns_gae(rewards_, dones_, old_state_estimates_.squeeze(-1))\n",
    "        \n",
    "        # Reshaping dims and prepping tensor types and locations. Nothing conceptually interesting.\n",
    "        returns_, old_state_estimates_, old_action_probs_, actions_, frames_ = reshaping_processing_acrobatics(returns_, old_state_estimates_, old_action_probs_, actions_, frames_)\n",
    "\n",
    "        return returns_, frames_, actions_, old_action_probs_, old_state_estimates_, epinfos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "VHIoA6EFE3ts",
    "outputId": "43b482fd-123c-435e-97da-44d5840e102e",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([18432]), torch.Size([18432, 3, 64, 64]), torch.Size([18432]))"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Setting global database of rollouts\n",
    "\n",
    "returns, frames, actions, old_action_probs, old_state_estimates, epinfos = gather_trajectories()\n",
    "returns.shape, frames.shape, actions.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "UKUUSN8KE3tu",
    "outputId": "9b096aab-543a-49ac-8ccb-9c97fb1eddbb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.2864077"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Grabbing scores in real-time during training. Procgen has so many random seeds we can\n",
    "# train and test on all of them without worrying about overlap btwn train and validate. \n",
    "\n",
    "get_avg_score(epinfos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2392c06ddc8>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# How good is our critic?\n",
    "\n",
    "plt.figure(figsize=(20,5))\n",
    "plt.plot(range(len(returns[:1000])), returns[:1000])\n",
    "plt.plot(range(len(old_state_estimates[:1000])), old_state_estimates[:1000])\n",
    "plt.title(\"Actual Returns vs Estimated Returns\")\n",
    "plt.legend([\"Actual\", \"Estimated\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 285
    },
    "colab_type": "code",
    "id": "lFlON2VHE3t4",
    "outputId": "adb604d7-db97-47a9-d424-9465549141a5"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x23929e5cbc8>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Check random imgs from rollouts database, ensure processing didn't mess anything up\n",
    "\n",
    "ix = random.randint(0, len(frames)-1)\n",
    "plt.imshow(pytorch_to_np_img(frames[ix:ix+1]).squeeze(0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training\n",
    "\n",
    "When authors say PPO they're ostensibly refering to [the original PPO paper](https://arxiv.org/abs/1707.06347). The trick described in the paper is to prevent the action probabilities generated by your current agent from deviating too far from the the probabilities generated by the agent who actually gathered the data. Paper does this simply by clipping the probabilities to a certain range, usually 20%.\n",
    "\n",
    "But what people REALLY mean when they say PPO is the above trick [combined with half a dozen others](https://arxiv.org/abs/1811.02553)--each one of which is critical to training success. \"PPO\" is better described as \"the basket of tricks used in actual code implementations like OpenAI Baselines\". "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 101
    },
    "colab_type": "code",
    "id": "cKFsTIUkE3t6",
    "outputId": "31f71a24-caf0-41ef-c14e-d13e8a835e0d",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([5, 3, 64, 64]),\n",
       " torch.Size([5]),\n",
       " torch.Size([5]),\n",
       " torch.Size([5, 15]),\n",
       " torch.Size([5]))"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_batch(batch_ix):\n",
    "    return frames[batch_ix].to(device), returns[batch_ix].to(device), actions[batch_ix].to(device), old_action_probs[batch_ix].to(device), old_state_estimates[batch_ix].to(device)\n",
    "\n",
    "ix = torch.randperm(len(returns))[:5];\n",
    "frames_batch, returns_batch, actions_batch, old_action_probs_batch, old_state_estimates_batch = get_batch(ix); \n",
    "frames_batch.shape, returns_batch.shape, actions_batch.shape, old_action_probs_batch.shape, old_state_estimates_batch.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "IWoAxC19E3t7",
    "outputId": "4d749512-69dd-43bf-d48d-40d5f661a400"
   },
   "outputs": [],
   "source": [
    "# Sample img as the agent will see it. Always check inputs as they are directly before agent ingestion\n",
    "\n",
    "frames_batch[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pc-mqbqBE3t9"
   },
   "outputs": [],
   "source": [
    "def calculate_action_gain_ppo(action_probs, old_action_probs, actions_taken, advantages, verbose=False):\n",
    "    \"\"\" Actions resulting in positive advantage made more probable, and vice versa. Do not allow to differ from old\n",
    "    probs by too much. Log probs are from current agent. Actions_taken are from OLD version of agent. \"\"\"\n",
    "    \n",
    "    a = actions_taken.unsqueeze(-1).to(device)\n",
    "    if verbose: print(\"\\n\\nactions unsqueezed\\n\", a)\n",
    "        \n",
    "    chosen_action_probs = action_probs.gather(1, a)\n",
    "    chosen_action_probs = chosen_action_probs.squeeze(-1)\n",
    "    \n",
    "    old_chosen_action_probs = old_action_probs.gather(1, a)\n",
    "    old_chosen_action_probs = old_chosen_action_probs.squeeze(-1)\n",
    "    if verbose: print(\"\\n\\nchosen action probs, new and old\\n\", chosen_action_probs,'\\n', old_chosen_action_probs, '\\n\\nadvantages values', advantages)\n",
    "    \n",
    "    ratio = torch.exp(chosen_action_probs - old_chosen_action_probs)\n",
    "    if verbose: print('\\n\\nratio', ratio)\n",
    "    \n",
    "    unclipped_action_gain = ratio * advantages\n",
    "    clipped_action_gain = torch.clamp(ratio, .8, 1.2) * advantages\n",
    "    if verbose: print('\\n\\nunclipped and clipped action gains\\n', unclipped_action_gain, '\\n', clipped_action_gain)\n",
    "    \n",
    "    action_gain = torch.min(unclipped_action_gain, clipped_action_gain)\n",
    "    if verbose: print('\\n\\n conservative lower bound action gain\\n', action_gain)\n",
    "        \n",
    "    action_gain = action_gain.mean()\n",
    "    if verbose: print('\\n\\nmean', action_gain)\n",
    "        \n",
    "    return action_gain # single scalar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 269
    },
    "colab_type": "code",
    "id": "5SRUBH-1E3t_",
    "outputId": "732847f6-6d70-485a-be97-1ddeba2dd5d8",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([5, 15])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with torch.no_grad():\n",
    "    action_probs, state_estimates = agent(frames_batch); \n",
    "\n",
    "action_probs.shape # (bs, n_actions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 622
    },
    "colab_type": "code",
    "id": "kGTq_QsRE3uF",
    "outputId": "c9c73a93-13d5-4682-cf93-06b1696d1c23",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0.6322, device='cuda:0')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test action gain calculations. Verbose true to inspect actual values.\n",
    "\n",
    "calculate_action_gain_ppo(action_probs, old_action_probs_batch, actions_batch, returns_batch, verbose=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 319
    },
    "colab_type": "code",
    "id": "hmZBSqWQE3uG",
    "outputId": "5cd247fb-2e5a-472d-9c67-bbcaa17c1dd3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor(0.7034, device='cuda:0')"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def get_critic_loss(old_state_estimates_b, state_estimates, returns_b, verbose=False):\n",
    "    \"\"\" How good is critic at estimating state? Don't allow to differ too much from old state estimates \"\"\"\n",
    "    \n",
    "    state_estimates = state_estimates.squeeze(-1)\n",
    "    clipped_state_estimate = old_state_estimates_b + torch.clamp(state_estimates - old_state_estimates_b, -.2, .2)\n",
    "    if verbose: print(\"\\nstate estimates, new and old\\n\\n\", state_estimates, '\\n', old_state_estimates_b, '\\n\\nreturns', returns_b)\n",
    "    critic_loss_1 = ((returns_b - clipped_state_estimate)**2)\n",
    "    critic_loss_2 = ((returns_b - state_estimates)**2)\n",
    "    critic_loss = torch.max(critic_loss_1, critic_loss_2)\n",
    "    if verbose: print('\\nCritic Losses: clipped, unclipped, conservative:\\n', critic_loss_1, '\\n', critic_loss_2, '\\n', critic_loss)\n",
    "    critic_loss = critic_loss.mean() * .5\n",
    "    return critic_loss # single scalar\n",
    "\n",
    "get_critic_loss(old_state_estimates_batch, state_estimates, returns_batch, False) # verbose true to inspect actual values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Vj55j_gJE3uK"
   },
   "outputs": [],
   "source": [
    "def get_entropy_bonus(action_probs):\n",
    "    \"\"\" Encourage humility \"\"\"\n",
    "    e = -(action_probs.exp() * (action_probs+1e-8))\n",
    "    e = e.sum(dim=1)\n",
    "    e = e.mean()\n",
    "    return e"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "D1hg_OAgE3uM"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor(2.7081, device='cuda:0'), tensor(2.7081, device='cuda:0'))"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pytorch distributions have entropy calc directly out of the box. Check against that.\n",
    "\n",
    "get_entropy_bonus(action_probs), categorical(action_probs.exp()).entropy().mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "pQF4Zv9aE3uS"
   },
   "outputs": [],
   "source": [
    "def run_batch(batch_ix, verbose=False):\n",
    "    \"\"\" \n",
    "    Run a single batch of data. Takes in indices, uses them to pull from global database of rollouts,\n",
    "    calculates and returns losses. No gradient steps here. \n",
    "    \"\"\"\n",
    "    \n",
    "    frames_b, returns_b, actions_taken_b, old_action_probs_b, old_state_estimates_b = get_batch(batch_ix)\n",
    "\n",
    "    action_probs, state_estimates = agent(frames_b)\n",
    "    \n",
    "    entropy_bonus = get_entropy_bonus(action_probs)\n",
    "    \n",
    "    critic_loss = get_critic_loss(old_state_estimates_b, state_estimates, returns_b, verbose=verbose)\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        advantages = (returns_b - state_estimates.squeeze(-1).detach()) # don't want to propogate gradients through this\n",
    "        advantages -= advantages.mean()\n",
    "        advantages /= (advantages.std() + 1e-8)\n",
    "    \n",
    "    action_gain = calculate_action_gain_ppo(action_probs, old_action_probs_b, actions_taken_b, advantages, verbose=verbose)\n",
    "    \n",
    "    return entropy_bonus, action_gain, critic_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YkB-25LbE3uT",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Manually run a batch\n",
    "\n",
    "with torch.no_grad():\n",
    "    run_batch(ix, False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "twyvUxMJE3uZ",
    "outputId": "cabb5d93-4cdf-4e39-b51a-21cf9e4ad0e4"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18432, 1356, 2304)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Hyperparameters all taken from procgen paper\n",
    "\n",
    "lr = 5e-4 # High lr bc batch size is big as well.\n",
    "opt = torch.optim.Adam(agent.parameters(), lr=lr, eps=1e-5)\n",
    "n_obs_per_round = n_envs * n_steps; \n",
    "target_n_obs = 10_000_000 # This is for 'easy' mode. 'Hard' requires 10x more. This is < half what procgen paper does, but is already enough to begin to converge. \n",
    "n_minibatches = 8\n",
    "bs = n_obs_per_round // n_minibatches\n",
    "n_rounds = target_n_obs // n_obs_per_round\n",
    "\n",
    "# procgen paper avgs gradients across 4 workers for training in 'hard' setting. This is same as making bs 4 times bigger. \n",
    "# For the same effect on a single GPU, use gradient accumulation to mimic larger batch size. medium.com/huggingface/training-larger-batches-practical-tips-on-1-gpu-multi-gpu-distributed-setups-ec88c3e51255\n",
    "accumulation_steps = 1 \n",
    "\n",
    "entropy_coef = .01\n",
    "value_loss_coef = .5\n",
    "n_opt_epochs = 3 # how many times does agent 'see' an observation before we discard it?\n",
    "\n",
    "n_obs_per_round, n_rounds, bs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "KrnnZfk4E3ub",
    "outputId": "1f397217-a477-49bf-90d5-3549c01792c4",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.5913889780640602"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def reflect(verbose=False):\n",
    "    \"\"\" Learn from current database of rollouts for a single epoch \"\"\"\n",
    "    \n",
    "    agent.train()\n",
    "    epoch_losses = []\n",
    "    ix_range = range(len(returns))\n",
    "    ix = 0\n",
    "    grad_accum_counter = 1\n",
    "    while ix < len(returns):\n",
    "        batch_ix = ix_range[ix:ix+bs]; ix += bs\n",
    "        entropy_bonus, action_gain, critic_loss = run_batch(batch_ix, verbose=verbose)\n",
    "        entropy_bonus *= entropy_coef\n",
    "        critic_loss *= value_loss_coef\n",
    "        if verbose: print(\"\\n\\nentropy bonus, action gain\\n, critic loss\\n\", entropy_bonus.item(), action_gain.item(), critic_loss.item())\n",
    "        total_loss = critic_loss - entropy_bonus - action_gain\n",
    "        total_loss /= accumulation_steps\n",
    "        total_loss.backward()\n",
    "                \n",
    "        if grad_accum_counter % accumulation_steps == 0:\n",
    "            nn.utils.clip_grad_norm_(agent.parameters(), .5);\n",
    "            opt.step()\n",
    "            opt.zero_grad()\n",
    "        grad_accum_counter+=1\n",
    "        epoch_losses.append(total_loss.item())\n",
    "\n",
    "    return np.array(epoch_losses).mean(); # epoch avg loss\n",
    "\n",
    "reflect(False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "def shuffle_database():\n",
    "    \"\"\" Shuffle the global database of rollouts in place. Doesn't return anything. Requires that database exist. \"\"\"\n",
    "    \n",
    "    global returns, frames, actions, old_action_probs, old_state_estimates\n",
    "    dataset_ix = torch.randperm(len(returns));\n",
    "    returns = returns[dataset_ix]\n",
    "    frames = frames[dataset_ix]\n",
    "    actions = actions[dataset_ix]\n",
    "    old_action_probs = old_action_probs[dataset_ix]\n",
    "    old_state_estimates = old_state_estimates[dataset_ix]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "Dq1nhwUEE3ue",
    "outputId": "bba11419-9656-4001-d0a9-f5c849629cab",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# Train. 'Easy' mode takes ~3 hours on single GPU. Go outside for a run.\n",
    "\n",
    "scores = []; losses = [];\n",
    "\n",
    "for i in range(n_rounds):\n",
    "    with torch.no_grad():\n",
    "        returns, frames, actions, old_action_probs, old_state_estimates, epinfos = gather_trajectories() # refresh 'database'\n",
    "        avg_score = get_avg_score(epinfos)\n",
    "        scores.append(avg_score)\n",
    "    \n",
    "    for e in range(n_opt_epochs):\n",
    "        shuffle_database()\n",
    "        loss = reflect(verbose = False)\n",
    "        losses.append(loss)\n",
    "    \n",
    "    if i % 100 == 0: print(i, avg_score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "l98cCvTzE3uf",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "torch.save(agent.state_dict(), env_name +\" agent.torch\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "colab_type": "code",
    "id": "ud1d3tzdE3uh",
    "outputId": "dfb58a38-7d25-4b96-81c4-6b401e06c724"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Scores starpilot')"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(range(len(scores)), scores)\n",
    "plt.title(\"Scores \" + env_name)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Manually Inspect Performance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Fhg4-NIuE3uo"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(torch.Size([18432]), torch.Size([18432, 3, 64, 64]))"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Generate some fresh data\n",
    "\n",
    "with torch.no_grad():\n",
    "    returns, frames, actions, old_action_probs, old_state_estimates, epinfos = gather_trajectories()\n",
    "    \n",
    "returns.shape, frames.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "n_vid_steps = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "q68ytRIRE3uq"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2392a0a3548>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(20,5))\n",
    "plt.plot(range(len(returns[:n_vid_steps])), returns[:n_vid_steps])\n",
    "plt.plot(range(len(returns[:n_vid_steps])), old_state_estimates[:n_vid_steps])\n",
    "plt.title(\"Actual Returns vs Estimated Returns\")\n",
    "plt.legend([\"Actual\", \"Estimated\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "l0R2K9E-E3up"
   },
   "outputs": [],
   "source": [
    "# Make a video so we can watch the agent play. Always do this.\n",
    "\n",
    "frames_np = frames[:n_vid_steps].numpy()\n",
    "returns_vid = returns[:n_vid_steps]; returns_vid -= returns_vid.min(); returns_vid /= returns_vid.max()\n",
    "\n",
    "height , width , layers =  64,64,3\n",
    "fps = 14\n",
    "video = cv2.VideoWriter(env_name + '_agent_run.avi', cv2.VideoWriter_fourcc(*\"MJPG\"), fps, (width,height))\n",
    "\n",
    "for i in range(n_vid_steps):\n",
    "    f = frames_np[i];\n",
    "    f = np.transpose(f, (1, 2, 0)) # prep axis numpy style\n",
    "    f = np.uint8(f*255)\n",
    "    r = returns_vid[i];\n",
    "    \n",
    "    # Adding a square in top-left to display actual returns. White is highest returns, black is lowest.\n",
    "    f[:5, :5, :] = 255 * r \n",
    "    \n",
    "    video.write(f)\n",
    "\n",
    "cv2.destroyAllWindows()\n",
    "video.release()\n",
    "\n",
    "# Video is now in root directory. If in colab, can download video with colab files functionality"
   ]
  }
 ],
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