How to use a custom Openai gym environment with Openai stable-baselines RL algorithms?

Question

I've been trying to use a custom openai gym environment for fixed wing uav from https://github.com/eivindeb/fixed-wing-gym by testing it with the openai stable-baselines algorithms but I have been running into issues for several days now. My baseline is the CartPole example Multiprocessing: Unleashing the Power of Vectorized Environments from https://stable-baselines.readthedocs.io/en/master/guide/examples.html#multiprocessing-unleashing-the-power-of-vectorized-environments since I would need to supply arguments and I am trying to use multiprocessing which I believe this example is all I need.

I have modified the baseline example as follows:

import gym
import numpy as np

from stable_baselines.common.policies import MlpPolicy
from stable_baselines.common.vec_env import SubprocVecEnv
from stable_baselines.common import set_global_seeds
from stable_baselines import ACKTR, PPO2
from gym_fixed_wing.fixed_wing import FixedWingAircraft


def make_env(env_id, rank, seed=0):
    """
    Utility function for multiprocessed env.

    :param env_id: (str) the environment ID
    :param num_env: (int) the number of environments you wish to have in subprocesses
    :param seed: (int) the inital seed for RNG
    :param rank: (int) index of the subprocess
    """

    def _init():
        env = FixedWingAircraft("fixed_wing_config.json")
        #env = gym.make(env_id)
        env.seed(seed + rank)
        return env

    set_global_seeds(seed)
    return _init

if __name__ == '__main__':
    env_id = "fixed_wing"
    #env_id = "CartPole-v1"
    num_cpu = 4  # Number of processes to use
    # Create the vectorized environment
    env = SubprocVecEnv([lambda: FixedWingAircraft for i in range(num_cpu)])
    #env = SubprocVecEnv([make_env(env_id, i) for i in range(num_cpu)])

    model = PPO2(MlpPolicy, env, verbose=1)
    model.learn(total_timesteps=25000)

    obs = env.reset()
    for _ in range(1000):
        action, _states = model.predict(obs)
        obs, rewards, dones, info = env.step(action)
        env.render()

and the error I keep getting is the following:

Traceback (most recent call last):
  File "/home/bonie/PycharmProjects/deepRL_fixedwing/fixed-wing-gym/gym_fixed_wing/ACKTR_fixedwing.py", line 38, in <module>
    model = PPO2(MlpPolicy, env, verbose=1)
  File "/home/bonie/PycharmProjects/deepRL_fixedwing/stable-baselines/stable_baselines/ppo2/ppo2.py", line 104, in __init__
    self.setup_model()
  File "/home/bonie/PycharmProjects/deepRL_fixedwing/stable-baselines/stable_baselines/ppo2/ppo2.py", line 134, in setup_model
    n_batch_step, reuse=False, **self.policy_kwargs)
  File "/home/bonie/PycharmProjects/deepRL_fixedwing/stable-baselines/stable_baselines/common/policies.py", line 660, in __init__
    feature_extraction="mlp", **_kwargs)
  File "/home/bonie/PycharmProjects/deepRL_fixedwing/stable-baselines/stable_baselines/common/policies.py", line 540, in __init__
    scale=(feature_extraction == "cnn"))
  File "/home/bonie/PycharmProjects/deepRL_fixedwing/stable-baselines/stable_baselines/common/policies.py", line 221, in __init__
    scale=scale)
  File "/home/bonie/PycharmProjects/deepRL_fixedwing/stable-baselines/stable_baselines/common/policies.py", line 117, in __init__
    self._obs_ph, self._processed_obs = observation_input(ob_space, n_batch, scale=scale)
  File "/home/bonie/PycharmProjects/deepRL_fixedwing/stable-baselines/stable_baselines/common/input.py", line 51, in observation_input
    type(ob_space).__name__))
NotImplementedError: Error: the model does not support input space of type NoneType

I am not sure what to really input as the env_id and for the def make_env(env_id, rank, seed=0) function. I am also thinking that the VecEnv function for parallel processes is not properly setup.

I am coding with Python v3.6 using PyCharm IDE in Ubuntu 18.04.

Any suggestions would really help at this point!

Thank you.

Answer 1

You created a custom environment alright, but you didn't register it with the openai gym interface. That's what the env_id refers to. All environments in gym can be set up by calling their registered name.

So basically what you need to do is follow the set up instructions here and create the appropriate __init__.py and setup.py scripts, and follow the same file structure.

At the end locally install your package using pip install -e . from within your environment directory.