mirror of
https://github.com/vale981/ray
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115 lines
4.5 KiB
Python
115 lines
4.5 KiB
Python
from unittest.mock import Mock, MagicMock
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import pytest
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from ray.rllib.examples.env.random_env import RandomEnv
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from ray.rllib.utils.pre_checks import check_gym_environments
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class TestGymCheckEnv():
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def test_has_observation_and_action_space(self):
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env = Mock(spec=[])
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with pytest.raises(
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AttributeError, match="Env must have observation_space."):
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check_gym_environments(env)
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env = Mock(spec=["observation_space"])
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with pytest.raises(
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AttributeError, match="Env must have action_space."):
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check_gym_environments(env)
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del env
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def test_obs_and_action_spaces_are_gym_spaces(self):
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env = RandomEnv()
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observation_space = env.observation_space
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env.observation_space = "not a gym space"
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with pytest.raises(
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ValueError, match="Observation space must be a gym.space"):
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check_gym_environments(env)
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env.observation_space = observation_space
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env.action_space = "not an action space"
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with pytest.raises(
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ValueError, match="Action space must be a gym.space"):
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check_gym_environments(env)
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del env
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def test_sampled_observation_contained(self):
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env = RandomEnv()
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# check for observation that is out of bounds
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error = ".*A sampled observation from your env wasn't contained .*"
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env.observation_space.sample = MagicMock(return_value=5)
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with pytest.raises(ValueError, match=error):
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check_gym_environments(env)
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# check for observation that is in bounds, but the wrong type
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env.observation_space.sample = MagicMock(return_value=float(1))
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with pytest.raises(ValueError, match=error):
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check_gym_environments(env)
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del env
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def test_sampled_action_contained(self):
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env = RandomEnv()
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error = ".*A sampled action from your env wasn't contained .*"
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env.action_space.sample = MagicMock(return_value=5)
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with pytest.raises(ValueError, match=error):
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check_gym_environments(env)
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# check for observation that is in bounds, but the wrong type
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env.action_space.sample = MagicMock(return_value=float(1))
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with pytest.raises(ValueError, match=error):
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check_gym_environments(env)
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del env
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def test_reset(self):
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reset = MagicMock(return_value=5)
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env = RandomEnv()
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env.reset = reset
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# check reset with out of bounds fails
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error = ".*The observation collected from env.reset().*"
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with pytest.raises(ValueError, match=error):
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check_gym_environments(env)
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# check reset with obs of incorrect type fails
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reset = MagicMock(return_value=float(1))
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env.reset = reset
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with pytest.raises(ValueError, match=error):
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check_gym_environments(env)
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del env
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def test_step(self):
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step = MagicMock(return_value=(5, 5, True, {}))
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env = RandomEnv()
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env.step = step
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error = ".*The observation collected from env.step.*"
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with pytest.raises(ValueError, match=error):
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check_gym_environments(env)
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# check reset that returns obs of incorrect type fails
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step = MagicMock(return_value=(float(1), 5, True, {}))
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env.step = step
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with pytest.raises(ValueError, match=error):
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check_gym_environments(env)
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# check step that returns reward of non float/int fails
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step = MagicMock(return_value=(1, "Not a valid reward", True, {}))
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env.step = step
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error = ("Your step function must return a reward that is integer or "
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"float.")
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with pytest.raises(AssertionError, match=error):
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check_gym_environments(env)
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# check step that returns a non bool fails
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step = MagicMock(
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return_value=(1, float(5), "not a valid done signal", {}))
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env.step = step
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error = "Your step function must return a done that is a boolean."
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with pytest.raises(AssertionError, match=error):
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check_gym_environments(env)
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# check step that returns a non dict fails
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step = MagicMock(
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return_value=(1, float(5), True, "not a valid env info"))
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env.step = step
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error = "Your step function must return a info that is a dict."
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with pytest.raises(AssertionError, match=error):
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check_gym_environments(env)
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del env
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if __name__ == "__main__":
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pytest.main()
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