# Source code for stable_baselines3.common.env_checker

```
import warnings
from typing import Any, Dict, Union
import gym
import numpy as np
from gym import spaces
from stable_baselines3.common.preprocessing import check_for_nested_spaces, is_image_space_channels_first
from stable_baselines3.common.vec_env import DummyVecEnv, VecCheckNan
def _is_numpy_array_space(space: spaces.Space) -> bool:
"""
Returns False if provided space is not representable as a single numpy array
(e.g. Dict and Tuple spaces return False)
"""
return not isinstance(space, (spaces.Dict, spaces.Tuple))
def _check_image_input(observation_space: spaces.Box, key: str = "") -> None:
"""
Check that the input will be compatible with Stable-Baselines
when the observation is apparently an image.
:param observation_space: Observation space
:key: When the observation space comes from a Dict space, we pass the
corresponding key to have more precise warning messages. Defaults to "".
"""
if observation_space.dtype != np.uint8:
warnings.warn(
f"It seems that your observation {key} is an image but its `dtype` "
f"is ({observation_space.dtype}) whereas it has to be `np.uint8`. "
"If your observation is not an image, we recommend you to flatten the observation "
"to have only a 1D vector"
)
if np.any(observation_space.low != 0) or np.any(observation_space.high != 255):
warnings.warn(
f"It seems that your observation space {key} is an image but the "
"upper and lower bounds are not in [0, 255]. "
"Because the CNN policy normalize automatically the observation "
"you may encounter issue if the values are not in that range."
)
non_channel_idx = 0
# Check only if width/height of the image is big enough
if is_image_space_channels_first(observation_space):
non_channel_idx = -1
if observation_space.shape[non_channel_idx] < 36 or observation_space.shape[1] < 36:
warnings.warn(
"The minimal resolution for an image is 36x36 for the default `CnnPolicy`. "
"You might need to use a custom features extractor "
"cf. https://stable-baselines3.readthedocs.io/en/master/guide/custom_policy.html"
)
def _check_unsupported_spaces(env: gym.Env, observation_space: spaces.Space, action_space: spaces.Space) -> None:
"""Emit warnings when the observation space or action space used is not supported by Stable-Baselines."""
if isinstance(observation_space, spaces.Dict):
nested_dict = False
for space in observation_space.spaces.values():
if isinstance(space, spaces.Dict):
nested_dict = True
if nested_dict:
warnings.warn(
"Nested observation spaces are not supported by Stable Baselines3 "
"(Dict spaces inside Dict space). "
"You should flatten it to have only one level of keys."
"For example, `dict(space1=dict(space2=Box(), space3=Box()), spaces4=Discrete())` "
"is not supported but `dict(space2=Box(), spaces3=Box(), spaces4=Discrete())` is."
)
if isinstance(observation_space, spaces.Tuple):
warnings.warn(
"The observation space is a Tuple,"
"this is currently not supported by Stable Baselines3. "
"However, you can convert it to a Dict observation space "
"(cf. https://github.com/openai/gym/blob/master/gym/spaces/dict.py). "
"which is supported by SB3."
)
if not _is_numpy_array_space(action_space):
warnings.warn(
"The action space is not based off a numpy array. Typically this means it's either a Dict or Tuple space. "
"This type of action space is currently not supported by Stable Baselines 3. You should try to flatten the "
"action using a wrapper."
)
def _check_nan(env: gym.Env) -> None:
"""Check for Inf and NaN using the VecWrapper."""
vec_env = VecCheckNan(DummyVecEnv([lambda: env]))
for _ in range(10):
action = np.array([env.action_space.sample()])
_, _, _, _ = vec_env.step(action)
def _is_goal_env(env: gym.Env) -> bool:
"""
Check if the env uses the convention for goal-conditioned envs (previously, the gym.GoalEnv interface)
"""
if isinstance(env, gym.Wrapper): # We need to unwrap the env since gym.Wrapper has the compute_reward method
return _is_goal_env(env.unwrapped)
return hasattr(env, "compute_reward")
def _check_goal_env_obs(obs: dict, observation_space: spaces.Dict, method_name: str) -> None:
"""
Check that an environment implementing the `compute_rewards()` method
(previously known as GoalEnv in gym) contains three elements,
namely `observation`, `desired_goal`, and `achieved_goal`.
"""
assert len(observation_space.spaces) == 3, (
"A goal conditioned env must contain 3 observation keys: `observation`, `desired_goal`, and `achieved_goal`."
f"The current observation contains {len(observation_space.spaces)} keys: {list(observation_space.spaces.keys())}"
)
for key in ["achieved_goal", "desired_goal"]:
if key not in observation_space.spaces:
raise AssertionError(
f"The observation returned by the `{method_name}()` method of a goal-conditioned env requires the '{key}' "
"key to be part of the observation dictionary. "
f"Current keys are {list(observation_space.spaces.keys())}"
)
def _check_goal_env_compute_reward(
obs: Dict[str, Union[np.ndarray, int]],
env: gym.Env,
reward: float,
info: Dict[str, Any],
):
"""
Check that reward is computed with `compute_reward`
and that the implementation is vectorized.
"""
achieved_goal, desired_goal = obs["achieved_goal"], obs["desired_goal"]
assert reward == env.compute_reward( # type: ignore[attr-defined]
achieved_goal, desired_goal, info
), "The reward was not computed with `compute_reward()`"
achieved_goal, desired_goal = np.array(achieved_goal), np.array(desired_goal)
batch_achieved_goals = np.array([achieved_goal, achieved_goal])
batch_desired_goals = np.array([desired_goal, desired_goal])
if isinstance(achieved_goal, int) or len(achieved_goal.shape) == 0:
batch_achieved_goals = batch_achieved_goals.reshape(2, 1)
batch_desired_goals = batch_desired_goals.reshape(2, 1)
batch_infos = np.array([info, info])
rewards = env.compute_reward(batch_achieved_goals, batch_desired_goals, batch_infos) # type: ignore[attr-defined]
assert rewards.shape == (2,), f"Unexpected shape for vectorized computation of reward: {rewards.shape} != (2,)"
assert rewards[0] == reward, f"Vectorized computation of reward differs from single computation: {rewards[0]} != {reward}"
def _check_obs(obs: Union[tuple, dict, np.ndarray, int], observation_space: spaces.Space, method_name: str) -> None:
"""
Check that the observation returned by the environment
correspond to the declared one.
"""
if not isinstance(observation_space, spaces.Tuple):
assert not isinstance(
obs, tuple
), f"The observation returned by the `{method_name}()` method should be a single value, not a tuple"
# The check for a GoalEnv is done by the base class
if isinstance(observation_space, spaces.Discrete):
assert isinstance(obs, int), f"The observation returned by `{method_name}()` method must be an int"
elif _is_numpy_array_space(observation_space):
assert isinstance(obs, np.ndarray), f"The observation returned by `{method_name}()` method must be a numpy array"
assert observation_space.contains(
obs
), f"The observation returned by the `{method_name}()` method does not match the given observation space"
def _check_box_obs(observation_space: spaces.Box, key: str = "") -> None:
"""
Check that the observation space is correctly formatted
when dealing with a ``Box()`` space. In particular, it checks:
- that the dimensions are big enough when it is an image, and that the type matches
- that the observation has an expected shape (warn the user if not)
"""
# If image, check the low and high values, the type and the number of channels
# and the shape (minimal value)
if len(observation_space.shape) == 3:
_check_image_input(observation_space, key)
if len(observation_space.shape) not in [1, 3]:
warnings.warn(
f"Your observation {key} has an unconventional shape (neither an image, nor a 1D vector). "
"We recommend you to flatten the observation "
"to have only a 1D vector or use a custom policy to properly process the data."
)
def _check_returned_values(env: gym.Env, observation_space: spaces.Space, action_space: spaces.Space) -> None:
"""
Check the returned values by the env when calling `.reset()` or `.step()` methods.
"""
# because env inherits from gym.Env, we assume that `reset()` and `step()` methods exists
obs = env.reset()
if _is_goal_env(env):
# Make mypy happy, already checked
assert isinstance(observation_space, spaces.Dict)
_check_goal_env_obs(obs, observation_space, "reset")
elif isinstance(observation_space, spaces.Dict):
assert isinstance(obs, dict), "The observation returned by `reset()` must be a dictionary"
if not obs.keys() == observation_space.spaces.keys():
raise AssertionError(
"The observation keys returned by `reset()` must match the observation "
f"space keys: {obs.keys()} != {observation_space.spaces.keys()}"
)
for key in observation_space.spaces.keys():
try:
_check_obs(obs[key], observation_space.spaces[key], "reset")
except AssertionError as e:
raise AssertionError(f"Error while checking key={key}: " + str(e)) from e
else:
_check_obs(obs, observation_space, "reset")
# Sample a random action
action = action_space.sample()
data = env.step(action)
assert len(data) == 4, "The `step()` method must return four values: obs, reward, done, info"
# Unpack
obs, reward, done, info = data
if _is_goal_env(env):
# Make mypy happy, already checked
assert isinstance(observation_space, spaces.Dict)
_check_goal_env_obs(obs, observation_space, "step")
_check_goal_env_compute_reward(obs, env, reward, info)
elif isinstance(observation_space, spaces.Dict):
assert isinstance(obs, dict), "The observation returned by `step()` must be a dictionary"
if not obs.keys() == observation_space.spaces.keys():
raise AssertionError(
"The observation keys returned by `step()` must match the observation "
f"space keys: {obs.keys()} != {observation_space.spaces.keys()}"
)
for key in observation_space.spaces.keys():
try:
_check_obs(obs[key], observation_space.spaces[key], "step")
except AssertionError as e:
raise AssertionError(f"Error while checking key={key}: " + str(e)) from e
else:
_check_obs(obs, observation_space, "step")
# We also allow int because the reward will be cast to float
assert isinstance(reward, (float, int)), "The reward returned by `step()` must be a float"
assert isinstance(done, bool), "The `done` signal must be a boolean"
assert isinstance(info, dict), "The `info` returned by `step()` must be a python dictionary"
# Goal conditioned env
if _is_goal_env(env):
assert reward == env.compute_reward(obs["achieved_goal"], obs["desired_goal"], info)
def _check_spaces(env: gym.Env) -> None:
"""
Check that the observation and action spaces are defined and inherit from spaces.Space. For
envs that follow the goal-conditioned standard (previously, the gym.GoalEnv interface) we check
the observation space is gym.spaces.Dict
"""
# Helper to link to the code, because gym has no proper documentation
gym_spaces = " cf https://github.com/openai/gym/blob/master/gym/spaces/"
assert hasattr(env, "observation_space"), "You must specify an observation space (cf gym.spaces)" + gym_spaces
assert hasattr(env, "action_space"), "You must specify an action space (cf gym.spaces)" + gym_spaces
assert isinstance(env.observation_space, spaces.Space), "The observation space must inherit from gym.spaces" + gym_spaces
assert isinstance(env.action_space, spaces.Space), "The action space must inherit from gym.spaces" + gym_spaces
if _is_goal_env(env):
assert isinstance(
env.observation_space, spaces.Dict
), "Goal conditioned envs (previously gym.GoalEnv) require the observation space to be gym.spaces.Dict"
# Check render cannot be covered by CI
def _check_render(env: gym.Env, warn: bool = True, headless: bool = False) -> None: # pragma: no cover
"""
Check the declared render modes and the `render()`/`close()`
method of the environment.
:param env: The environment to check
:param warn: Whether to output additional warnings
:param headless: Whether to disable render modes
that require a graphical interface. False by default.
"""
render_modes = env.metadata.get("render.modes")
if render_modes is None:
if warn:
warnings.warn(
"No render modes was declared in the environment "
" (env.metadata['render.modes'] is None or not defined), "
"you may have trouble when calling `.render()`"
)
else:
# Don't check render mode that require a
# graphical interface (useful for CI)
if headless and "human" in render_modes:
render_modes.remove("human")
# Check all declared render modes
for render_mode in render_modes:
env.render(mode=render_mode)
env.close()
[docs]def check_env(env: gym.Env, warn: bool = True, skip_render_check: bool = True) -> None:
"""
Check that an environment follows Gym API.
This is particularly useful when using a custom environment.
Please take a look at https://github.com/openai/gym/blob/master/gym/core.py
for more information about the API.
It also optionally check that the environment is compatible with Stable-Baselines.
:param env: The Gym environment that will be checked
:param warn: Whether to output additional warnings
mainly related to the interaction with Stable Baselines
:param skip_render_check: Whether to skip the checks for the render method.
True by default (useful for the CI)
"""
assert isinstance(
env, gym.Env
), "Your environment must inherit from the gym.Env class cf https://github.com/openai/gym/blob/master/gym/core.py"
# ============= Check the spaces (observation and action) ================
_check_spaces(env)
# Define aliases for convenience
observation_space = env.observation_space
action_space = env.action_space
# Warn the user if needed.
# A warning means that the environment may run but not work properly with Stable Baselines algorithms
if warn:
_check_unsupported_spaces(env, observation_space, action_space)
obs_spaces = observation_space.spaces if isinstance(observation_space, spaces.Dict) else {"": observation_space}
for key, space in obs_spaces.items():
if isinstance(space, spaces.Box):
_check_box_obs(space, key)
# Check for the action space, it may lead to hard-to-debug issues
if isinstance(action_space, spaces.Box) and (
np.any(np.abs(action_space.low) != np.abs(action_space.high))
or np.any(action_space.low != -1)
or np.any(action_space.high != 1)
):
warnings.warn(
"We recommend you to use a symmetric and normalized Box action space (range=[-1, 1]) "
"cf https://stable-baselines3.readthedocs.io/en/master/guide/rl_tips.html"
)
if isinstance(action_space, spaces.Box):
assert np.all(
np.isfinite(np.array([action_space.low, action_space.high]))
), "Continuous action space must have a finite lower and upper bound"
if isinstance(action_space, spaces.Box) and action_space.dtype != np.dtype(np.float32):
warnings.warn(
f"Your action space has dtype {action_space.dtype}, we recommend using np.float32 to avoid cast errors."
)
# ============ Check the returned values ===============
_check_returned_values(env, observation_space, action_space)
# ==== Check the render method and the declared render modes ====
if not skip_render_check:
_check_render(env, warn=warn) # pragma: no cover
try:
check_for_nested_spaces(env.observation_space)
# The check doesn't support nested observations/dict actions
# A warning about it has already been emitted
_check_nan(env)
except NotImplementedError:
pass
```