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| """A set of functions for passively checking environment implementations.""" | |
| import inspect | |
| from functools import partial | |
| from typing import Callable | |
| import numpy as np | |
| from gym import Space, error, logger, spaces | |
| def _check_box_observation_space(observation_space: spaces.Box): | |
| """Checks that a :class:`Box` observation space is defined in a sensible way. | |
| Args: | |
| observation_space: A box observation space | |
| """ | |
| # Check if the box is an image | |
| if len(observation_space.shape) == 3: | |
| if observation_space.dtype != np.uint8: | |
| logger.warn( | |
| f"It seems a Box observation space is an image but the `dtype` is not `np.uint8`, actual type: {observation_space.dtype}. " | |
| "If the Box observation space is not an image, we recommend flattening the observation to have only a 1D vector." | |
| ) | |
| if np.any(observation_space.low != 0) or np.any(observation_space.high != 255): | |
| logger.warn( | |
| "It seems a Box observation space is an image but the upper and lower bounds are not in [0, 255]. " | |
| "Generally, CNN policies assume observations are within that range, so you may encounter an issue if the observation values are not." | |
| ) | |
| if len(observation_space.shape) not in [1, 3]: | |
| logger.warn( | |
| "A Box observation space has an unconventional shape (neither an image, nor a 1D vector). " | |
| "We recommend flattening the observation to have only a 1D vector or use a custom policy to properly process the data. " | |
| f"Actual observation shape: {observation_space.shape}" | |
| ) | |
| assert ( | |
| observation_space.low.shape == observation_space.shape | |
| ), f"The Box observation space shape and low shape have different shapes, low shape: {observation_space.low.shape}, box shape: {observation_space.shape}" | |
| assert ( | |
| observation_space.high.shape == observation_space.shape | |
| ), f"The Box observation space shape and high shape have have different shapes, high shape: {observation_space.high.shape}, box shape: {observation_space.shape}" | |
| if np.any(observation_space.low == observation_space.high): | |
| logger.warn("A Box observation space maximum and minimum values are equal.") | |
| elif np.any(observation_space.high < observation_space.low): | |
| logger.warn("A Box observation space low value is greater than a high value.") | |
| def _check_box_action_space(action_space: spaces.Box): | |
| """Checks that a :class:`Box` action space is defined in a sensible way. | |
| Args: | |
| action_space: A box action space | |
| """ | |
| assert ( | |
| action_space.low.shape == action_space.shape | |
| ), f"The Box action space shape and low shape have have different shapes, low shape: {action_space.low.shape}, box shape: {action_space.shape}" | |
| assert ( | |
| action_space.high.shape == action_space.shape | |
| ), f"The Box action space shape and high shape have different shapes, high shape: {action_space.high.shape}, box shape: {action_space.shape}" | |
| if np.any(action_space.low == action_space.high): | |
| logger.warn("A Box action space maximum and minimum values are equal.") | |
| elif np.any(action_space.high < action_space.low): | |
| logger.warn("A Box action space low value is greater than a high value.") | |
| def check_space( | |
| space: Space, space_type: str, check_box_space_fn: Callable[[spaces.Box], None] | |
| ): | |
| """A passive check of the environment action space that should not affect the environment.""" | |
| if not isinstance(space, spaces.Space): | |
| raise AssertionError( | |
| f"{space_type} space does not inherit from `gym.spaces.Space`, actual type: {type(space)}" | |
| ) | |
| elif isinstance(space, spaces.Box): | |
| check_box_space_fn(space) | |
| elif isinstance(space, spaces.Discrete): | |
| assert ( | |
| 0 < space.n | |
| ), f"Discrete {space_type} space's number of elements must be positive, actual number of elements: {space.n}" | |
| assert ( | |
| space.shape == () | |
| ), f"Discrete {space_type} space's shape should be empty, actual shape: {space.shape}" | |
| elif isinstance(space, spaces.MultiDiscrete): | |
| assert ( | |
| space.shape == space.nvec.shape | |
| ), f"Multi-discrete {space_type} space's shape must be equal to the nvec shape, space shape: {space.shape}, nvec shape: {space.nvec.shape}" | |
| assert np.all( | |
| 0 < space.nvec | |
| ), f"Multi-discrete {space_type} space's all nvec elements must be greater than 0, actual nvec: {space.nvec}" | |
| elif isinstance(space, spaces.MultiBinary): | |
| assert np.all( | |
| 0 < np.asarray(space.shape) | |
| ), f"Multi-binary {space_type} space's all shape elements must be greater than 0, actual shape: {space.shape}" | |
| elif isinstance(space, spaces.Tuple): | |
| assert 0 < len( | |
| space.spaces | |
| ), f"An empty Tuple {space_type} space is not allowed." | |
| for subspace in space.spaces: | |
| check_space(subspace, space_type, check_box_space_fn) | |
| elif isinstance(space, spaces.Dict): | |
| assert 0 < len( | |
| space.spaces.keys() | |
| ), f"An empty Dict {space_type} space is not allowed." | |
| for subspace in space.values(): | |
| check_space(subspace, space_type, check_box_space_fn) | |
| check_observation_space = partial( | |
| check_space, | |
| space_type="observation", | |
| check_box_space_fn=_check_box_observation_space, | |
| ) | |
| check_action_space = partial( | |
| check_space, space_type="action", check_box_space_fn=_check_box_action_space | |
| ) | |
| def check_obs(obs, observation_space: spaces.Space, method_name: str): | |
| """Check that the observation returned by the environment correspond to the declared one. | |
| Args: | |
| obs: The observation to check | |
| observation_space: The observation space of the observation | |
| method_name: The method name that generated the observation | |
| """ | |
| pre = f"The obs returned by the `{method_name}()` method" | |
| if isinstance(observation_space, spaces.Discrete): | |
| if not isinstance(obs, (np.int64, int)): | |
| logger.warn(f"{pre} should be an int or np.int64, actual type: {type(obs)}") | |
| elif isinstance(observation_space, spaces.Box): | |
| if observation_space.shape != (): | |
| if not isinstance(obs, np.ndarray): | |
| logger.warn( | |
| f"{pre} was expecting a numpy array, actual type: {type(obs)}" | |
| ) | |
| elif obs.dtype != observation_space.dtype: | |
| logger.warn( | |
| f"{pre} was expecting numpy array dtype to be {observation_space.dtype}, actual type: {obs.dtype}" | |
| ) | |
| elif isinstance(observation_space, (spaces.MultiBinary, spaces.MultiDiscrete)): | |
| if not isinstance(obs, np.ndarray): | |
| logger.warn(f"{pre} was expecting a numpy array, actual type: {type(obs)}") | |
| elif isinstance(observation_space, spaces.Tuple): | |
| if not isinstance(obs, tuple): | |
| logger.warn(f"{pre} was expecting a tuple, actual type: {type(obs)}") | |
| assert len(obs) == len( | |
| observation_space.spaces | |
| ), f"{pre} length is not same as the observation space length, obs length: {len(obs)}, space length: {len(observation_space.spaces)}" | |
| for sub_obs, sub_space in zip(obs, observation_space.spaces): | |
| check_obs(sub_obs, sub_space, method_name) | |
| elif isinstance(observation_space, spaces.Dict): | |
| assert isinstance(obs, dict), f"{pre} must be a dict, actual type: {type(obs)}" | |
| assert ( | |
| obs.keys() == observation_space.spaces.keys() | |
| ), f"{pre} observation keys is not same as the observation space keys, obs keys: {list(obs.keys())}, space keys: {list(observation_space.spaces.keys())}" | |
| for space_key in observation_space.spaces.keys(): | |
| check_obs(obs[space_key], observation_space[space_key], method_name) | |
| try: | |
| if obs not in observation_space: | |
| logger.warn(f"{pre} is not within the observation space.") | |
| except Exception as e: | |
| logger.warn(f"{pre} is not within the observation space with exception: {e}") | |
| def env_reset_passive_checker(env, **kwargs): | |
| """A passive check of the `Env.reset` function investigating the returning reset information and returning the data unchanged.""" | |
| signature = inspect.signature(env.reset) | |
| if "seed" not in signature.parameters and "kwargs" not in signature.parameters: | |
| logger.warn( | |
| "Future gym versions will require that `Env.reset` can be passed a `seed` instead of using `Env.seed` for resetting the environment random number generator." | |
| ) | |
| else: | |
| seed_param = signature.parameters.get("seed") | |
| # Check the default value is None | |
| if seed_param is not None and seed_param.default is not None: | |
| logger.warn( | |
| "The default seed argument in `Env.reset` should be `None`, otherwise the environment will by default always be deterministic. " | |
| f"Actual default: {seed_param}" | |
| ) | |
| if "options" not in signature.parameters and "kwargs" not in signature.parameters: | |
| logger.warn( | |
| "Future gym versions will require that `Env.reset` can be passed `options` to allow the environment initialisation to be passed additional information." | |
| ) | |
| # Checks the result of env.reset with kwargs | |
| result = env.reset(**kwargs) | |
| if not isinstance(result, tuple): | |
| logger.warn( | |
| f"The result returned by `env.reset()` was not a tuple of the form `(obs, info)`, where `obs` is a observation and `info` is a dictionary containing additional information. Actual type: `{type(result)}`" | |
| ) | |
| elif len(result) != 2: | |
| logger.warn( | |
| "The result returned by `env.reset()` should be `(obs, info)` by default, , where `obs` is a observation and `info` is a dictionary containing additional information." | |
| ) | |
| else: | |
| obs, info = result | |
| check_obs(obs, env.observation_space, "reset") | |
| assert isinstance( | |
| info, dict | |
| ), f"The second element returned by `env.reset()` was not a dictionary, actual type: {type(info)}" | |
| return result | |
| def env_step_passive_checker(env, action): | |
| """A passive check for the environment step, investigating the returning data then returning the data unchanged.""" | |
| # We don't check the action as for some environments then out-of-bounds values can be given | |
| result = env.step(action) | |
| assert isinstance( | |
| result, tuple | |
| ), f"Expects step result to be a tuple, actual type: {type(result)}" | |
| if len(result) == 4: | |
| logger.deprecation( | |
| "Core environment is written in old step API which returns one bool instead of two. " | |
| "It is recommended to rewrite the environment with new step API. " | |
| ) | |
| obs, reward, done, info = result | |
| if not isinstance(done, (bool, np.bool8)): | |
| logger.warn( | |
| f"Expects `done` signal to be a boolean, actual type: {type(done)}" | |
| ) | |
| elif len(result) == 5: | |
| obs, reward, terminated, truncated, info = result | |
| # np.bool is actual python bool not np boolean type, therefore bool_ or bool8 | |
| if not isinstance(terminated, (bool, np.bool8)): | |
| logger.warn( | |
| f"Expects `terminated` signal to be a boolean, actual type: {type(terminated)}" | |
| ) | |
| if not isinstance(truncated, (bool, np.bool8)): | |
| logger.warn( | |
| f"Expects `truncated` signal to be a boolean, actual type: {type(truncated)}" | |
| ) | |
| else: | |
| raise error.Error( | |
| f"Expected `Env.step` to return a four or five element tuple, actual number of elements returned: {len(result)}." | |
| ) | |
| check_obs(obs, env.observation_space, "step") | |
| if not ( | |
| np.issubdtype(type(reward), np.integer) | |
| or np.issubdtype(type(reward), np.floating) | |
| ): | |
| logger.warn( | |
| f"The reward returned by `step()` must be a float, int, np.integer or np.floating, actual type: {type(reward)}" | |
| ) | |
| else: | |
| if np.isnan(reward): | |
| logger.warn("The reward is a NaN value.") | |
| if np.isinf(reward): | |
| logger.warn("The reward is an inf value.") | |
| assert isinstance( | |
| info, dict | |
| ), f"The `info` returned by `step()` must be a python dictionary, actual type: {type(info)}" | |
| return result | |
| def env_render_passive_checker(env, *args, **kwargs): | |
| """A passive check of the `Env.render` that the declared render modes/fps in the metadata of the environment is declared.""" | |
| render_modes = env.metadata.get("render_modes") | |
| if render_modes is None: | |
| logger.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: | |
| if not isinstance(render_modes, (list, tuple)): | |
| logger.warn( | |
| f"Expects the render_modes to be a sequence (i.e. list, tuple), actual type: {type(render_modes)}" | |
| ) | |
| elif not all(isinstance(mode, str) for mode in render_modes): | |
| logger.warn( | |
| f"Expects all render modes to be strings, actual types: {[type(mode) for mode in render_modes]}" | |
| ) | |
| render_fps = env.metadata.get("render_fps") | |
| # We only require `render_fps` if rendering is actually implemented | |
| if len(render_modes) > 0: | |
| if render_fps is None: | |
| logger.warn( | |
| "No render fps was declared in the environment (env.metadata['render_fps'] is None or not defined), rendering may occur at inconsistent fps." | |
| ) | |
| else: | |
| if not ( | |
| np.issubdtype(type(render_fps), np.integer) | |
| or np.issubdtype(type(render_fps), np.floating) | |
| ): | |
| logger.warn( | |
| f"Expects the `env.metadata['render_fps']` to be an integer or a float, actual type: {type(render_fps)}" | |
| ) | |
| else: | |
| assert ( | |
| render_fps > 0 | |
| ), f"Expects the `env.metadata['render_fps']` to be greater than zero, actual value: {render_fps}" | |
| # env.render is now an attribute with default None | |
| if len(render_modes) == 0: | |
| assert ( | |
| env.render_mode is None | |
| ), f"With no render_modes, expects the Env.render_mode to be None, actual value: {env.render_mode}" | |
| else: | |
| assert env.render_mode is None or env.render_mode in render_modes, ( | |
| "The environment was initialized successfully however with an unsupported render mode. " | |
| f"Render mode: {env.render_mode}, modes: {render_modes}" | |
| ) | |
| result = env.render(*args, **kwargs) | |
| # TODO: Check that the result is correct | |
| return result | |