Update : Missing observation space while linking Model with environment

[benjamin-arfa]

Code:

import energym
from energym.examples.Controller import LabController
from energym.wrappers.rl_wrapper import RLWrapper
import gym
from stable_baselines3 import PPO

envName = "Apartments2Thermal-v0"
env = energym.make(envName, weather = "ESP_CT_Barcelona", simulation_days=300)
reward = lambda output : 1/(abs(output['Z01_T']-22)+0.1)
env_RL = RLWrapper(env, reward)
inputs = env_RL.get_inputs_names()
model = PPO('MlpPolicy', env_RL, verbose=1)
model.learn(total_timesteps=4800)

Here is the error traceback:

---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-142-2f18c2afcb80> in <module>
----> 1 model = PPO('MlpPolicy', env_RL, verbose=1)
      2 model.learn(total_timesteps=4800)

~/.local/lib/python3.8/site-packages/stable_baselines3/ppo/ppo.py in __init__(self, policy, env, learning_rate, n_steps, batch_size, n_epochs, gamma, gae_lambda, clip_range, clip_range_vf, ent_coef, vf_coef, max_grad_norm, use_sde, sde_sample_freq, target_kl, tensorboard_log, create_eval_env, policy_kwargs, verbose, seed, device, _init_setup_model)
     90     ):
     91 
---> 92         super(PPO, self).__init__(
     93             policy,
     94             env,

~/.local/lib/python3.8/site-packages/stable_baselines3/common/on_policy_algorithm.py in __init__(self, policy, env, learning_rate, n_steps, gamma, gae_lambda, ent_coef, vf_coef, max_grad_norm, use_sde, sde_sample_freq, tensorboard_log, create_eval_env, monitor_wrapper, policy_kwargs, verbose, seed, device, _init_setup_model)
     72     ):
     73 
---> 74         super(OnPolicyAlgorithm, self).__init__(
     75             policy=policy,
     76             env=env,

~/.local/lib/python3.8/site-packages/stable_baselines3/common/base_class.py in __init__(self, policy, env, policy_base, learning_rate, policy_kwargs, tensorboard_log, verbose, device, support_multi_env, create_eval_env, monitor_wrapper, seed, use_sde, sde_sample_freq)
    155 
    156             env = maybe_make_env(env, monitor_wrapper, self.verbose)
--> 157             env = self._wrap_env(env, self.verbose)
    158 
    159             self.observation_space = env.observation_space

~/.local/lib/python3.8/site-packages/stable_baselines3/common/base_class.py in _wrap_env(env, verbose)
    175             if verbose >= 1:
    176                 print("Wrapping the env in a DummyVecEnv.")
--> 177             env = DummyVecEnv([lambda: env])
    178 
    179         if is_image_space(env.observation_space) and not is_wrapped(env, VecTransposeImage):

~/.local/lib/python3.8/site-packages/stable_baselines3/common/vec_env/dummy_vec_env.py in __init__(self, env_fns)
     25         self.envs = [fn() for fn in env_fns]
     26         env = self.envs[0]
---> 27         VecEnv.__init__(self, len(env_fns), env.observation_space, env.action_space)
     28         obs_space = env.observation_space
     29         self.keys, shapes, dtypes = obs_space_info(obs_space)

/usr/local/lib/python3.8/dist-packages/energym-0.1-py3.8.egg/energym/envs/env.py in __getattr__(self, name)
    164                 "attempted to get missing private attribute '{}'".format(name)
    165             )
--> 166         return getattr(self.env, name)
    167 
    168     @classmethod

AttributeError: 'Apartments2' object has no attribute 'observation_space'

[pscharnho]

It would indeed be nice to directly use stable baselines for Energym, I'll have a look into modifying the RLWrapper to make it work.

[pscharnho]

I created a new branch for the modified RLWrapper (2-modify-rlwrapper), you can test the version from there and if everything works fine, we will close this.