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e913bc81b8
* feat: add `isort` to `pre-commit` * ci: skip `__init__.py` file for `isort` * ci: make `isort` mandatory in lint pipeline * docs: add a section on Git hooks * ci: check isort diff * fix: isort from master branch * docs: add pre-commit badge * ci: update black + bandit versions * feat: add PR template * refactor: PR template * ci: remove bandit * docs: add Black badge * ci: try to remove all `|| true` statements * ci: remove lint_python job - Remove `lint_python` CI job - Move `pyupgrade` job to `pre-commit` workflow * fix: avoid messing with typing * docs: add a note on running `pre-cpmmit` manually * ci: apply `pre-commit` to the whole codebase
104 lines
3.2 KiB
Python
104 lines
3.2 KiB
Python
import numpy as np
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import pytest
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from gym import envs
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from gym.spaces import Box
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from gym.utils.env_checker import check_env
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from tests.envs.spec_list import spec_list
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# This runs a smoketest on each official registered env. We may want
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# to try also running environments which are not officially registered
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# envs.
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@pytest.mark.filterwarnings(
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"ignore:.*We recommend you to use a symmetric and normalized Box action space.*"
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)
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@pytest.mark.parametrize("spec", spec_list)
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def test_env(spec):
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# Capture warnings
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with pytest.warns(None) as warnings:
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env = spec.make()
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# Test if env adheres to Gym API
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check_env(env, warn=True, skip_render_check=True)
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# Check that dtype is explicitly declared for gym.Box spaces
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for warning_msg in warnings:
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assert "autodetected dtype" not in str(warning_msg.message)
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ob_space = env.observation_space
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act_space = env.action_space
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ob = env.reset()
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assert ob_space.contains(ob), f"Reset observation: {ob!r} not in space"
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if isinstance(ob_space, Box):
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# Only checking dtypes for Box spaces to avoid iterating through tuple entries
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assert (
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ob.dtype == ob_space.dtype
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), f"Reset observation dtype: {ob.dtype}, expected: {ob_space.dtype}"
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a = act_space.sample()
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observation, reward, done, _info = env.step(a)
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assert ob_space.contains(
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observation
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), f"Step observation: {observation!r} not in space"
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assert np.isscalar(reward), f"{reward} is not a scalar for {env}"
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assert isinstance(done, bool), f"Expected {done} to be a boolean"
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if isinstance(ob_space, Box):
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assert (
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observation.dtype == ob_space.dtype
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), f"Step observation dtype: {ob.dtype}, expected: {ob_space.dtype}"
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for mode in env.metadata.get("render_modes", []):
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env.render(mode=mode)
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# Make sure we can render the environment after close.
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for mode in env.metadata.get("render_modes", []):
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env.render(mode=mode)
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env.close()
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@pytest.mark.parametrize("spec", spec_list)
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def test_reset_info(spec):
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with pytest.warns(None) as warnings:
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env = spec.make()
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ob_space = env.observation_space
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obs = env.reset()
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assert ob_space.contains(obs)
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obs = env.reset(return_info=False)
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assert ob_space.contains(obs)
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obs, info = env.reset(return_info=True)
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assert ob_space.contains(obs)
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assert isinstance(info, dict)
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env.close()
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# Run a longer rollout on some environments
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def test_random_rollout():
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for env in [envs.make("CartPole-v1"), envs.make("FrozenLake-v1")]:
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agent = lambda ob: env.action_space.sample()
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ob = env.reset()
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for _ in range(10):
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assert env.observation_space.contains(ob)
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a = agent(ob)
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assert env.action_space.contains(a)
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(ob, _reward, done, _info) = env.step(a)
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if done:
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break
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env.close()
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def test_env_render_result_is_immutable():
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environs = [
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envs.make("Taxi-v3"),
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envs.make("FrozenLake-v1"),
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]
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for env in environs:
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env.reset()
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output = env.render(mode="ansi")
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assert isinstance(output, str)
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env.close()
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