mirror of
https://github.com/saymrwulf/stable-baselines3.git
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856da19609
* add check to ensure action space is non-dict non-tuple for env_checker nan check * update changelog.rst * add regression test for new check * commit-checks * add more action space checks * update docstrings * add warning check
245 lines
10 KiB
Python
245 lines
10 KiB
Python
import warnings
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from typing import Union
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import gym
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import numpy as np
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from gym import spaces
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from stable_baselines3.common.vec_env import DummyVecEnv, VecCheckNan
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def _is_numpy_array_space(space: spaces.Space) -> bool:
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"""
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Returns False if provided space is not representable as a single numpy array
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(e.g. Dict and Tuple spaces return False)
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"""
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return not isinstance(space, (spaces.Dict, spaces.Tuple))
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def _check_image_input(observation_space: spaces.Box) -> None:
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"""
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Check that the input will be compatible with Stable-Baselines
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when the observation is apparently an image.
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"""
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if observation_space.dtype != np.uint8:
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warnings.warn(
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"It seems that your observation is an image but the `dtype` "
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"of your observation_space is not `np.uint8`. "
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"If your observation is not an image, we recommend you to flatten the observation "
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"to have only a 1D vector"
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)
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if np.any(observation_space.low != 0) or np.any(observation_space.high != 255):
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warnings.warn(
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"It seems that your observation space is an image but the "
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"upper and lower bounds are not in [0, 255]. "
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"Because the CNN policy normalize automatically the observation "
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"you may encounter issue if the values are not in that range."
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)
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if observation_space.shape[0] < 36 or observation_space.shape[1] < 36:
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warnings.warn(
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"The minimal resolution for an image is 36x36 for the default CnnPolicy. "
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"You might need to use a custom `cnn_extractor` "
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"cf https://stable-baselines3.readthedocs.io/en/master/guide/custom_policy.html"
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)
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def _check_unsupported_spaces(env: gym.Env, observation_space: spaces.Space, action_space: spaces.Space) -> None:
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"""Emit warnings when the observation space or action space used is not supported by Stable-Baselines."""
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if isinstance(observation_space, spaces.Dict) and not isinstance(env, gym.GoalEnv):
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warnings.warn(
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"The observation space is a Dict but the environment is not a gym.GoalEnv "
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"(cf https://github.com/openai/gym/blob/master/gym/core.py), "
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"this is currently not supported by Stable Baselines "
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"(cf https://github.com/hill-a/stable-baselines/issues/133), "
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"you will need to use a custom policy. "
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)
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if isinstance(observation_space, spaces.Tuple):
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warnings.warn(
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"The observation space is a Tuple,"
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"this is currently not supported by Stable Baselines "
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"(cf https://github.com/hill-a/stable-baselines/issues/133), "
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"you will need to flatten the observation and maybe use a custom policy. "
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)
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if not _is_numpy_array_space(action_space):
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warnings.warn(
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"The action space is not based off a numpy array. Typically this means it's either a Dict or Tuple space. "
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"This type of action space is currently not supported by Stable Baselines 3. You should try to flatten the "
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"action using a wrapper."
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)
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def _check_nan(env: gym.Env) -> None:
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"""Check for Inf and NaN using the VecWrapper."""
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vec_env = VecCheckNan(DummyVecEnv([lambda: env]))
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for _ in range(10):
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action = np.array([env.action_space.sample()])
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_, _, _, _ = vec_env.step(action)
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def _check_obs(obs: Union[tuple, dict, np.ndarray, int], observation_space: spaces.Space, method_name: str) -> None:
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"""
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Check that the observation returned by the environment
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correspond to the declared one.
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"""
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if not isinstance(observation_space, spaces.Tuple):
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assert not isinstance(
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obs, tuple
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), "The observation returned by the `{}()` method should be a single value, not a tuple".format(method_name)
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# The check for a GoalEnv is done by the base class
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if isinstance(observation_space, spaces.Discrete):
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assert isinstance(obs, int), "The observation returned by `{}()` method must be an int".format(method_name)
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elif _is_numpy_array_space(observation_space):
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assert isinstance(obs, np.ndarray), "The observation returned by `{}()` method must be a numpy array".format(
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method_name
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)
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assert observation_space.contains(
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obs
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), "The observation returned by the `{}()` method does not match the given observation space".format(method_name)
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def _check_returned_values(env: gym.Env, observation_space: spaces.Space, action_space: spaces.Space) -> None:
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"""
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Check the returned values by the env when calling `.reset()` or `.step()` methods.
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"""
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# because env inherits from gym.Env, we assume that `reset()` and `step()` methods exists
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obs = env.reset()
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_check_obs(obs, observation_space, "reset")
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# Sample a random action
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action = action_space.sample()
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data = env.step(action)
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assert len(data) == 4, "The `step()` method must return four values: obs, reward, done, info"
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# Unpack
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obs, reward, done, info = data
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_check_obs(obs, observation_space, "step")
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# We also allow int because the reward will be cast to float
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assert isinstance(reward, (float, int)), "The reward returned by `step()` must be a float"
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assert isinstance(done, bool), "The `done` signal must be a boolean"
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assert isinstance(info, dict), "The `info` returned by `step()` must be a python dictionary"
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if isinstance(env, gym.GoalEnv):
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# For a GoalEnv, the keys are checked at reset
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assert reward == env.compute_reward(obs["achieved_goal"], obs["desired_goal"], info)
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def _check_spaces(env: gym.Env) -> None:
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"""
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Check that the observation and action spaces are defined
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and inherit from gym.spaces.Space.
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"""
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# Helper to link to the code, because gym has no proper documentation
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gym_spaces = " cf https://github.com/openai/gym/blob/master/gym/spaces/"
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assert hasattr(env, "observation_space"), "You must specify an observation space (cf gym.spaces)" + gym_spaces
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assert hasattr(env, "action_space"), "You must specify an action space (cf gym.spaces)" + gym_spaces
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assert isinstance(env.observation_space, spaces.Space), "The observation space must inherit from gym.spaces" + gym_spaces
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assert isinstance(env.action_space, spaces.Space), "The action space must inherit from gym.spaces" + gym_spaces
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def _check_render(env: gym.Env, warn: bool = True, headless: bool = False) -> None:
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"""
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Check the declared render modes and the `render()`/`close()`
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method of the environment.
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:param env: The environment to check
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:param warn: Whether to output additional warnings
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:param headless: Whether to disable render modes
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that require a graphical interface. False by default.
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"""
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render_modes = env.metadata.get("render.modes")
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if render_modes is None:
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if warn:
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warnings.warn(
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"No render modes was declared in the environment "
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" (env.metadata['render.modes'] is None or not defined), "
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"you may have trouble when calling `.render()`"
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)
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else:
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# Don't check render mode that require a
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# graphical interface (useful for CI)
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if headless and "human" in render_modes:
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render_modes.remove("human")
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# Check all declared render modes
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for render_mode in render_modes:
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env.render(mode=render_mode)
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env.close()
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def check_env(env: gym.Env, warn: bool = True, skip_render_check: bool = True) -> None:
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"""
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Check that an environment follows Gym API.
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This is particularly useful when using a custom environment.
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Please take a look at https://github.com/openai/gym/blob/master/gym/core.py
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for more information about the API.
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It also optionally check that the environment is compatible with Stable-Baselines.
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:param env: The Gym environment that will be checked
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:param warn: Whether to output additional warnings
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mainly related to the interaction with Stable Baselines
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:param skip_render_check: Whether to skip the checks for the render method.
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True by default (useful for the CI)
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"""
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assert isinstance(
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env, gym.Env
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), "Your environment must inherit from the gym.Env class cf https://github.com/openai/gym/blob/master/gym/core.py"
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# ============= Check the spaces (observation and action) ================
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_check_spaces(env)
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# Define aliases for convenience
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observation_space = env.observation_space
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action_space = env.action_space
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# Warn the user if needed.
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# A warning means that the environment may run but not work properly with Stable Baselines algorithms
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if warn:
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_check_unsupported_spaces(env, observation_space, action_space)
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# If image, check the low and high values, the type and the number of channels
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# and the shape (minimal value)
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if isinstance(observation_space, spaces.Box) and len(observation_space.shape) == 3:
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_check_image_input(observation_space)
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if isinstance(observation_space, spaces.Box) and len(observation_space.shape) not in [1, 3]:
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warnings.warn(
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"Your observation has an unconventional shape (neither an image, nor a 1D vector). "
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"We recommend you to flatten the observation "
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"to have only a 1D vector"
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)
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# Check for the action space, it may lead to hard-to-debug issues
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if isinstance(action_space, spaces.Box) and (
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np.any(np.abs(action_space.low) != np.abs(action_space.high))
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or np.any(np.abs(action_space.low) > 1)
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or np.any(np.abs(action_space.high) > 1)
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):
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warnings.warn(
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"We recommend you to use a symmetric and normalized Box action space (range=[-1, 1]) "
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"cf https://stable-baselines3.readthedocs.io/en/master/guide/rl_tips.html"
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)
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# ============ Check the returned values ===============
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_check_returned_values(env, observation_space, action_space)
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# ==== Check the render method and the declared render modes ====
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if not skip_render_check:
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_check_render(env, warn=warn)
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# The check only works with numpy arrays
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if _is_numpy_array_space(observation_space) and _is_numpy_array_space(action_space):
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_check_nan(env)
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