mirror of
https://github.com/Farama-Foundation/Gymnasium.git
synced 2025-08-22 07:02:19 +00:00
15049e22d7
* initial draft of optional info dict in reset function, implemented for cartpole, tests seem to be passing * merged core.py * updated return type annotation for reset function in core.py * optional metadata with return_info from reset added for all first party environments, with corresponding tests. Incomplete implementation for wrappers and vector wrappers * removed Optional type for return_info arguments * added tests for return_info to normalize wrapper and sync_vector_env * autoformatted using black * added optional reset metadata tests to several wrappers * added return_info capability to async_vector_env.py and test to verify functionality * added optional return_info test for record_video.py * removed tests for mujoco environments * autoformatted * improved test coverage for optional reset return_info * re-removed unit test envs accidentally reintroduced in merge * removed unnecessary import * changes based on code-review * small fix to core wrapper typing and autoformatted record_epsisode_stats * small change to pass flake8 style
135 lines
3.9 KiB
Python
135 lines
3.9 KiB
Python
from typing import Optional
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import gym
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import numpy as np
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from numpy.testing import assert_almost_equal
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from gym.wrappers.normalize import NormalizeObservation, NormalizeReward
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class DummyRewardEnv(gym.Env):
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metadata = {}
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def __init__(self, return_reward_idx=0):
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self.action_space = gym.spaces.Discrete(2)
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self.observation_space = gym.spaces.Box(
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low=np.array([-1.0]), high=np.array([1.0])
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)
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self.returned_rewards = [0, 1, 2, 3, 4]
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self.return_reward_idx = return_reward_idx
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self.t = self.return_reward_idx
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def step(self, action):
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self.t += 1
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return np.array([self.t]), self.t, self.t == len(self.returned_rewards), {}
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def reset(
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self,
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*,
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seed: Optional[int] = None,
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return_info: Optional[bool] = False,
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options: Optional[dict] = None
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):
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super().reset(seed=seed)
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self.t = self.return_reward_idx
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if not return_info:
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return np.array([self.t])
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else:
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return np.array([self.t]), {}
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def make_env(return_reward_idx):
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def thunk():
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env = DummyRewardEnv(return_reward_idx)
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return env
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return thunk
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def test_normalize_observation():
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env = DummyRewardEnv(return_reward_idx=0)
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env = NormalizeObservation(env)
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env.reset()
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env.step(env.action_space.sample())
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assert_almost_equal(env.obs_rms.mean, 0.5, decimal=4)
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env.step(env.action_space.sample())
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assert_almost_equal(env.obs_rms.mean, 1.0, decimal=4)
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def test_normalize_reset_info():
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env = DummyRewardEnv(return_reward_idx=0)
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env = NormalizeObservation(env)
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obs = env.reset()
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assert isinstance(obs, np.ndarray)
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del obs
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obs = env.reset(return_info=False)
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assert isinstance(obs, np.ndarray)
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del obs
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obs, info = env.reset(return_info=True)
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assert isinstance(obs, np.ndarray)
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assert isinstance(info, dict)
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def test_normalize_return():
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env = DummyRewardEnv(return_reward_idx=0)
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env = NormalizeReward(env)
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env.reset()
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env.step(env.action_space.sample())
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assert_almost_equal(
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env.return_rms.mean,
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np.mean([1]), # [first return]
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decimal=4,
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)
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env.step(env.action_space.sample())
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assert_almost_equal(
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env.return_rms.mean,
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np.mean([2 + env.gamma * 1, 1]), # [second return, first return]
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decimal=4,
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)
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def test_normalize_observation_vector_env():
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env_fns = [make_env(0), make_env(1)]
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envs = gym.vector.SyncVectorEnv(env_fns)
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envs.reset()
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obs, reward, _, _ = envs.step(envs.action_space.sample())
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np.testing.assert_almost_equal(obs, np.array([[1], [2]]), decimal=4)
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np.testing.assert_almost_equal(reward, np.array([1, 2]), decimal=4)
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env_fns = [make_env(0), make_env(1)]
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envs = gym.vector.SyncVectorEnv(env_fns)
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envs = NormalizeObservation(envs)
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envs.reset()
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assert_almost_equal(
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envs.obs_rms.mean,
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np.mean([0.5]), # the mean of first observations [[0, 1]]
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decimal=4,
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)
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obs, reward, _, _ = envs.step(envs.action_space.sample())
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assert_almost_equal(
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envs.obs_rms.mean,
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np.mean([1.0]), # the mean of first and second observations [[0, 1], [1, 2]]
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decimal=4,
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)
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def test_normalize_return_vector_env():
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env_fns = [make_env(0), make_env(1)]
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envs = gym.vector.SyncVectorEnv(env_fns)
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envs = NormalizeReward(envs)
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obs = envs.reset()
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obs, reward, _, _ = envs.step(envs.action_space.sample())
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assert_almost_equal(
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envs.return_rms.mean,
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np.mean([1.5]), # the mean of first returns [[1, 2]]
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decimal=4,
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)
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obs, reward, _, _ = envs.step(envs.action_space.sample())
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assert_almost_equal(
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envs.return_rms.mean,
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np.mean(
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[[1, 2], [2 + envs.gamma * 1, 3 + envs.gamma * 2]]
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), # the mean of first and second returns [[1, 2], [2 + envs.gamma * 1, 3 + envs.gamma * 2]]
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decimal=4,
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)
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