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56 lines
1.8 KiB
Python
56 lines
1.8 KiB
Python
"""Test suite for NormalizeRewardV1."""
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import numpy as np
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from gymnasium.core import ActType
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from gymnasium.experimental.wrappers import NormalizeRewardV1
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from tests.testing_env import GenericTestEnv
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def _make_reward_env():
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"""Function that returns a `GenericTestEnv` with reward=1."""
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def step_func(self, action: ActType):
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return self.observation_space.sample(), 1.0, False, False, {}
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return GenericTestEnv(step_func=step_func)
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def test_running_mean_normalize_reward_wrapper():
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"""Tests that the property `_update_running_mean` freezes/continues the running statistics updating."""
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env = _make_reward_env()
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wrapped_env = NormalizeRewardV1(env)
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# Default value is True
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assert wrapped_env.update_running_mean
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wrapped_env.reset()
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rms_var_init = wrapped_env.rewards_running_means.var
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rms_mean_init = wrapped_env.rewards_running_means.mean
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# Statistics are updated when env.step()
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wrapped_env.step(None)
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rms_var_updated = wrapped_env.rewards_running_means.var
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rms_mean_updated = wrapped_env.rewards_running_means.mean
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assert rms_var_init != rms_var_updated
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assert rms_mean_init != rms_mean_updated
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# Assure property is set
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wrapped_env.update_running_mean = False
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assert not wrapped_env.update_running_mean
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# Statistics are frozen
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wrapped_env.step(None)
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assert rms_var_updated == wrapped_env.rewards_running_means.var
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assert rms_mean_updated == wrapped_env.rewards_running_means.mean
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def test_normalize_reward_wrapper():
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"""Tests that the NormalizeReward does not throw an error."""
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# TODO: Functional correctness should be tested
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env = _make_reward_env()
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wrapped_env = NormalizeRewardV1(env)
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wrapped_env.reset()
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_, reward, _, _, _ = wrapped_env.step(None)
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assert np.ndim(reward) == 0
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env.close()
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