def test_semisuper_succeeds():
"""Regression test. Ensure that this can write"""
with helpers.tempdir() as temp:
env = gym.make('SemisuperPendulumDecay-v0')
env = Monitor(env, temp)
env.reset()
env.step(env.action_space.sample())
env.close()
def test_video_callable_false_does_not_record():
with helpers.tempdir() as temp:
env = gym.make('CartPole-v0')
env = Monitor(env, temp, video_callable=False)
env.reset()
env.close()
results = monitoring.load_results(temp)
assert len(results['videos']) == 0
def test_video_callable_records_videos():
with helpers.tempdir() as temp:
env = gym.make('CartPole-v0')
env = Monitor(env, temp)
env.reset()
env.close()
results = monitoring.load_results(temp)
assert len(results['videos']) == 1, "Videos: {}".format(results['videos'])
def test_monitor_filename():
with helpers.tempdir() as temp:
env = gym.make('CartPole-v0')
env = Monitor(env, directory=temp)
env.close()
manifests = glob.glob(os.path.join(temp, '*.manifest.*'))
assert len(manifests) == 1
def test_video_callable_true_not_allowed():
with helpers.tempdir() as temp:
env = gym.make('CartPole-v0')
try:
env = Monitor(env, temp, video_callable=True)
except error.Error:
pass
else:
assert False
def test_write_upon_reset_false():
with helpers.tempdir() as temp:
env = gym.make('CartPole-v0')
env = Monitor(env, directory=temp, video_callable=False, write_upon_reset=False)
env.reset()
files = glob.glob(os.path.join(temp, '*'))
assert not files, "Files: {}".format(files)
env.close()
files = glob.glob(os.path.join(temp, '*'))
assert len(files) > 0
def test_vnc_monitoring():
with helpers.tempdir() as temp:
env = gym.make('gym-core.Pong-v3')
env.configure(remotes=2)
env = wrappers.GymCoreAction(env)
env = wrappers.Monitor(env, temp)
env.reset()
for i in range(2):
env.step([0, 0])
env.close()
results = gym.monitoring.load_results(temp)
assert results['env_info']['env_id'] == 'gym-core.Pong-v3'
def test_multiprocessing_env_monitoring():
with helpers.tempdir() as temp:
env = wrappers.WrappedMultiprocessingEnv('Pong-v3')
env.configure(n=2)
env = wrappers.Monitor(env, temp)
env.reset()
for i in range(2):
env.step([0, 0])
env.close()
manifests = glob.glob(os.path.join(temp, '*.video.*'))
assert len(manifests) == 2, 'There are {} manifests: {}'.format(
len(manifests), manifests)
results = gym.monitoring.load_results(temp)
assert results['env_info']['env_id'] == 'Pong-v3'
def test_steps_limit_restart():
with helpers.tempdir() as temp:
env = gym.make('test.StepsLimitCartpole-v0')
env = Monitor(env, temp, video_callable=False)
env.reset()
# Episode has started
_, _, done, info = env.step(env.action_space.sample())
assert done == False
# Limit reached, now we get a done signal and the env resets itself
_, _, done, info = env.step(env.action_space.sample())
assert done == True
assert env.episode_id == 1
env.close()
def test():
benchmark = registration.Benchmark(id='MyBenchmark-v0',
scorer=scoring.ClipTo01ThenAverage(),
tasks=[{
'env_id': 'CartPole-v0',
'trials': 1,
'max_timesteps': 5
}, {
'env_id': 'CartPole-v0',
'trials': 1,
'max_timesteps': 100,
}])
with helpers.tempdir() as temp:
env = gym.make('CartPole-v0')
env = wrappers.Monitor(env, directory=temp, video_callable=False)
env.seed(0)
env.set_monitor_mode('evaluation')
rollout(env)
env.set_monitor_mode('training')
for i in range(2):
rollout(env)
env.set_monitor_mode('evaluation')
rollout(env, good=True)
env.close()
results = monitoring.load_results(temp)
evaluation_score = benchmark.score_evaluation(
'CartPole-v0', results['data_sources'],
results['initial_reset_timestamps'], results['episode_lengths'],
results['episode_rewards'], results['episode_types'],
results['timestamps'])
benchmark_score = benchmark.score_benchmark({
'CartPole-v0':
evaluation_score['scores'],
})
assert np.all(
np.isclose(evaluation_score['scores'],
[0.00089999999999999998, 0.0054000000000000003
])), "evaluation_score={}".format(evaluation_score)
assert np.isclose(
benchmark_score,
0.00315), "benchmark_score={}".format(benchmark_score)
def test_only_complete_episodes_written():
with helpers.tempdir() as temp:
env = gym.make('CartPole-v0')
env = Monitor(env, temp, video_callable=False)
env.reset()
d = False
while not d:
_, _, d, _ = env.step(env.action_space.sample())
env.reset()
env.step(env.action_space.sample())
env.close()
# Only 1 episode should be written
results = monitoring.load_results(temp)
assert len(results['episode_lengths']) == 1, "Found {} episodes written; expecting 1".format(len(results['episode_lengths']))
def test_env_reuse():
with helpers.tempdir() as temp:
env = gym.make('Autoreset-v0')
env = Monitor(env, temp)
env.reset()
_, _, done, _ = env.step(None)
assert not done
_, _, done, _ = env.step(None)
assert done
_, _, done, _ = env.step(None)
assert not done
_, _, done, _ = env.step(None)
assert done
env.close()
def test_no_monitor_reset_unless_done():
def assert_reset_raises(env):
errored = False
try:
env.reset()
except error.Error:
errored = True
assert errored, "Env allowed a reset when it shouldn't have"
with helpers.tempdir() as temp:
# Make sure we can reset as we please without monitor
env = gym.make('CartPole-v0')
env.reset()
env.step(env.action_space.sample())
env.step(env.action_space.sample())
env.reset()
# can reset once as soon as we start
env = Monitor(env, temp, video_callable=False)
env.reset()
# can reset multiple times in a row
env.reset()
env.reset()
env.step(env.action_space.sample())
env.step(env.action_space.sample())
assert_reset_raises(env)
# should allow resets after the episode is done
d = False
while not d:
_, _, d, _ = env.step(env.action_space.sample())
env.reset()
env.reset()
env.step(env.action_space.sample())
assert_reset_raises(env)
env.close()
