mirror of
https://github.com/Farama-Foundation/Gymnasium.git
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4c460ba6c8
* add dtype to Box
* remove board_game, debugging, safety, parameter_tuning environments
* massive set of breaking changes
- remove python logging module
- _step, _reset, _seed, _close => non underscored method
- remove benchmark and scoring folder
* Improve render("human"), now resizable, closable window.
* get rid of default step and reset in wrappers, so it doesn’t silently fail for people with underscore methods
* CubeCrash unit test environment
* followup fixes
* MemorizeDigits unit test envrionment
* refactored spaces a bit
fixed indentation
disabled test_env_semantics
* fix unit tests
* fixes
* CubeCrash, MemorizeDigits tested
* gym backwards compatibility patch
* gym backwards compatibility, followup fixes
* changelist, add spaces to main namespaces
* undo_logger_setup for backwards compat
* remove configuration.py
44 lines
1.5 KiB
Python
44 lines
1.5 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.envs.tests.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.parametrize("spec", spec_list)
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def test_env(spec):
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env = spec.make()
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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), 'Reset observation: {!r} not in space'.format(ob)
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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(observation), 'Step observation: {!r} not in space'.format(observation)
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assert np.isscalar(reward), "{} is not a scalar for {}".format(reward, env)
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assert isinstance(done, bool), "Expected {} to be a boolean".format(done)
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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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# 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-v0'), envs.make('FrozenLake-v0')]:
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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: break
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env.close()
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