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https://github.com/Farama-Foundation/Gymnasium.git
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134de4a713
* Updated testing requirements based off extra["testing"] * Updated setup to check the version is valid, added testing and all dependency groups and collects the requirements from requirements.txt to keep everything standardized. * Updated requirements.txt based on the current minimum gym requirements.txt to work * Updated requirements.txt based on the current minimum gym requirements.txt to work * Updated test_requirements.txt based on the current gym full testing requirements * Pre-commit updates * Add integer check for the `n` parameter * The type of self.spaces is an Iterable which is absorbed by the tuple. * Simplifies the environment checker to two files, env_checker.py and passive_env_checker.py with a new wrapper env_checker.py * Adds the passive environment checker on `gym.make` * Ignore the `check_env` warn parameter * Ignore the `check_env` warn parameter * Use the `data_equivalence` function * Revert rewrite setup.py changes * Remove smart formatting for 3.6 support * Fixed `check_action_space` and `check_observation_space` * Added disable_env_checker to vector.make such that env_checker would only run on the first environment created. * Removing check that different seeds would produce different initialising states * Use the unwrapped environment np_random * Fixed vector environment creator
84 lines
2.8 KiB
Python
84 lines
2.8 KiB
Python
"""Test environment determinism by performing a rollout."""
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import pytest
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from gym.utils.env_checker import data_equivalence
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from tests.envs.spec_list import spec_list
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@pytest.mark.parametrize("spec", spec_list, ids=[spec.id for spec in spec_list])
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def test_env(spec):
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"""Run a rollout with two environments and assert equality.
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This test run a rollout of NUM_STEPS steps with two environments
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initialized with the same seed and assert that:
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- observation after first reset are the same
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- same actions are sampled by the two envs
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- observations are contained in the observation space
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- obs, rew, done and info are equals between the two envs
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Args:
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spec (EnvSpec): Environment specification
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"""
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# Note that this precludes running this test in multiple
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# threads. However, we probably already can't do multithreading
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# due to some environments.
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SEED = 0
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NUM_STEPS = 50
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env1, env2 = spec.make(), spec.make()
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initial_observation1 = env1.reset(seed=SEED)
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initial_observation2 = env2.reset(seed=SEED)
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env1.action_space.seed(SEED)
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env2.action_space.seed(SEED)
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assert data_equivalence(
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initial_observation1, initial_observation2
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), f"Initial Observations 1 and 2 are not equivalent. initial obs 1={initial_observation1}, initial obs 2={initial_observation2}"
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for i in range(NUM_STEPS):
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action1 = env1.action_space.sample()
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action2 = env2.action_space.sample()
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try:
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assert data_equivalence(
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action1, action2
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), f"Action 1 and 2 are not equivalent. action 1={action1}, action 2={action2}"
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except AssertionError:
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print(f"env 1 action space={env1.action_space}")
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print(f"env 2 action space={env2.action_space}")
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print(f"[{i}] action sample 1={action1}, action sample 2={action2}")
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raise
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# Don't check rollout equality if it's a nondeterministic
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# environment.
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if spec.nondeterministic:
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return
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obs1, rew1, done1, info1 = env1.step(action1)
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obs2, rew2, done2, info2 = env2.step(action2)
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assert data_equivalence(
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obs1, obs2
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), f"Observation 1 and 2 are not equivalent. obs 1={obs1}, obs 2={obs2}"
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assert env1.observation_space.contains(obs1)
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assert env2.observation_space.contains(obs2)
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assert rew1 == rew2, f"[{i}] reward1: {rew1}, reward2: {rew2}"
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assert done1 == done2, f"[{i}] done1: {done1}, done2: {done2}"
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assert data_equivalence(
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info1, info2
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), f"Info 1 and 2 are not equivalent. info 1={info1}, info 2={info2}"
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if done1: # done2 verified in previous assertion
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env1.reset(seed=SEED)
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env2.reset(seed=SEED)
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env1.close()
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env2.close()
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