6c6c872523
Changes: - Disclaimer in README - Replaced all occurences of Gym with Gymnasium - Removed code that is now dead since we no longer need to support the old step API - Updated type hints to only allow new step API - Increased required version of envpool to support Gymnasium - Increased required version of PettingZoo to support Gymnasium - Updated `PettingZooEnv` to only use the new step API, removed hack to also support old API - I had to add some `# type: ignore` comments, due to new type hinting in Gymnasium. I'm not that familiar with type hinting but I believe that the issue is on the Gymnasium side and we are looking into it. - Had to update `MyTestEnv` to support `options` kwarg - Skip NNI tests because they still use OpenAI Gym - Also allow `PettingZooEnv` in vector environment - Updated doc page about ReplayBuffer to also talk about terminated and truncated flags. Still need to do: - Update the Jupyter notebooks in docs - Check the entire code base for more dead code (from compatibility stuff) - Check the reset functions of all environments/wrappers in code base to make sure they use the `options` kwarg - Someone might want to check test_env_finite.py - Is it okay to allow `PettingZooEnv` in vector environments? Might need to update docs?
82 lines
3.1 KiB
Python
82 lines
3.1 KiB
Python
from typing import Any, Dict, List, SupportsFloat, Tuple, Union
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import gymnasium as gym
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import numpy as np
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from packaging import version
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class ContinuousToDiscrete(gym.ActionWrapper):
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"""Gym environment wrapper to take discrete action in a continuous environment.
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:param gym.Env env: gym environment with continuous action space.
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:param int action_per_dim: number of discrete actions in each dimension
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of the action space.
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"""
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def __init__(self, env: gym.Env, action_per_dim: Union[int, List[int]]) -> None:
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super().__init__(env)
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assert isinstance(env.action_space, gym.spaces.Box)
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low, high = env.action_space.low, env.action_space.high
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if isinstance(action_per_dim, int):
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action_per_dim = [action_per_dim] * env.action_space.shape[0]
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assert len(action_per_dim) == env.action_space.shape[0]
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self.action_space = gym.spaces.MultiDiscrete(action_per_dim)
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self.mesh = np.array(
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[np.linspace(lo, hi, a) for lo, hi, a in zip(low, high, action_per_dim)],
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dtype=object
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)
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def action(self, act: np.ndarray) -> np.ndarray: # type: ignore
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# modify act
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assert len(act.shape) <= 2, f"Unknown action format with shape {act.shape}."
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if len(act.shape) == 1:
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return np.array([self.mesh[i][a] for i, a in enumerate(act)])
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return np.array([[self.mesh[i][a] for i, a in enumerate(a_)] for a_ in act])
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class MultiDiscreteToDiscrete(gym.ActionWrapper):
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"""Gym environment wrapper to take discrete action in multidiscrete environment.
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:param gym.Env env: gym environment with multidiscrete action space.
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"""
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def __init__(self, env: gym.Env) -> None:
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super().__init__(env)
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assert isinstance(env.action_space, gym.spaces.MultiDiscrete)
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nvec = env.action_space.nvec
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assert nvec.ndim == 1
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self.bases = np.ones_like(nvec)
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for i in range(1, len(self.bases)):
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self.bases[i] = self.bases[i - 1] * nvec[-i]
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self.action_space = gym.spaces.Discrete(np.prod(nvec))
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def action(self, act: np.ndarray) -> np.ndarray: # type: ignore
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converted_act = []
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for b in np.flip(self.bases):
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converted_act.append(act // b)
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act = act % b
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return np.array(converted_act).transpose()
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class TruncatedAsTerminated(gym.Wrapper):
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"""A wrapper that set ``terminated = terminated or truncated`` for ``step()``.
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It's intended to use with ``gym.wrappers.TimeLimit``.
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:param gym.Env env: gym environment.
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"""
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def __init__(self, env: gym.Env):
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super().__init__(env)
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if not version.parse(gym.__version__) >= version.parse('0.26.0'):
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raise EnvironmentError(
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f"TruncatedAsTerminated is not applicable with gym version \
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{gym.__version__}"
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)
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def step(self,
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act: np.ndarray) -> Tuple[Any, SupportsFloat, bool, bool, Dict[str, Any]]:
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observation, reward, terminated, truncated, info = super().step(act)
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terminated = (terminated or truncated)
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return observation, reward, terminated, truncated, info
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