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Gymnasium/gym/envs/tests/test_envs.py

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import pytest
import numpy as np
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from gym import envs
from gym.envs.tests.spec_list import spec_list
# This runs a smoketest on each official registered env. We may want
# to try also running environments which are not officially registered
# envs.
@pytest.mark.parametrize("spec", spec_list)
def test_env(spec):
# Capture warnings
with pytest.warns(None) as warnings:
env = spec.make()
# Check that dtype is explicitly declared for gym.Box spaces
for warning_msg in warnings:
assert not 'autodetected dtype' in str(warning_msg.message)
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ob_space = env.observation_space
act_space = env.action_space
ob = env.reset()
assert ob_space.contains(ob), 'Reset observation: {!r} not in space'.format(ob)
a = act_space.sample()
observation, reward, done, _info = env.step(a)
assert ob_space.contains(observation), 'Step observation: {!r} not in space'.format(observation)
assert np.isscalar(reward), "{} is not a scalar for {}".format(reward, env)
assert isinstance(done, bool), "Expected {} to be a boolean".format(done)
for mode in env.metadata.get('render.modes', []):
env.render(mode=mode)
# Make sure we can render the environment after close.
for mode in env.metadata.get('render.modes', []):
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env.render(mode=mode)
env.close()
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# Run a longer rollout on some environments
def test_random_rollout():
for env in [envs.make('CartPole-v0'), envs.make('FrozenLake-v0')]:
agent = lambda ob: env.action_space.sample()
ob = env.reset()
for _ in range(10):
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assert env.observation_space.contains(ob)
a = agent(ob)
assert env.action_space.contains(a)
(ob, _reward, done, _info) = env.step(a)
if done: break
env.close()
def test_env_render_result_is_immutable():
environs = [
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envs.make('Taxi-v3'),
envs.make('FrozenLake-v0'),
envs.make('Reverse-v0'),
]
for env in environs:
env.reset()
output = env.render(mode='ansi')
assert isinstance(output, str)
env.close()