def test_update_envs_env_update():
max_episode_length = 16
env = GarageEnv(PointEnv())
policy = FixedPolicy(env.spec,
scripted_actions=[
env.action_space.sample()
for _ in range(max_episode_length)
])
tasks = SetTaskSampler(PointEnv)
n_workers = 8
workers = WorkerFactory(seed=100,
max_episode_length=max_episode_length,
n_workers=n_workers)
sampler = MultiprocessingSampler.from_worker_factory(workers, policy, env)
rollouts = sampler.obtain_samples(0,
161,
np.asarray(policy.get_param_values()),
env_update=tasks.sample(n_workers))
mean_rewards = []
goals = []
for rollout in rollouts.split():
mean_rewards.append(rollout.rewards.mean())
goals.append(rollout.env_infos['task'][0]['goal'])
assert np.var(mean_rewards) > 0
assert np.var(goals) > 0
with pytest.raises(ValueError):
sampler.obtain_samples(0,
10,
np.asarray(policy.get_param_values()),
env_update=tasks.sample(n_workers + 1))
sampler.shutdown_worker()
env.close()
def test_init_with_crashed_worker():
max_episode_length = 16
env = GarageEnv(PointEnv())
policy = FixedPolicy(env.spec,
scripted_actions=[
env.action_space.sample()
for _ in range(max_episode_length)
])
tasks = SetTaskSampler(lambda: GarageEnv(PointEnv()))
n_workers = 2
workers = WorkerFactory(seed=100,
max_episode_length=max_episode_length,
n_workers=n_workers)
class CrashingPolicy:
def reset(self, **kwargs):
raise Exception('Intentional subprocess crash')
bad_policy = CrashingPolicy()
# This causes worker 2 to crash.
sampler = MultiprocessingSampler.from_worker_factory(
workers, [policy, bad_policy], envs=tasks.sample(n_workers))
rollouts = sampler.obtain_samples(0, 160, None)
assert sum(rollouts.lengths) >= 160
sampler.shutdown_worker()
env.close()
def test_obtain_exact_trajectories():
max_episode_length = 15
n_workers = 8
env = GarageEnv(PointEnv())
per_worker_actions = [env.action_space.sample() for _ in range(n_workers)]
policies = [
FixedPolicy(env.spec, [action] * max_episode_length)
for action in per_worker_actions
]
workers = WorkerFactory(seed=100,
max_episode_length=max_episode_length,
n_workers=n_workers)
sampler = MultiprocessingSampler.from_worker_factory(workers,
policies,
envs=env)
n_traj_per_worker = 3
rollouts = sampler.obtain_exact_trajectories(n_traj_per_worker,
agent_update=policies)
# At least one action per trajectory.
assert sum(rollouts.lengths) >= n_workers * n_traj_per_worker
# All of the trajectories.
assert len(rollouts.lengths) == n_workers * n_traj_per_worker
worker = -1
for count, rollout in enumerate(rollouts.split()):
if count % n_traj_per_worker == 0:
worker += 1
assert (rollout.actions == per_worker_actions[worker]).all()
sampler.shutdown_worker()
env.close()
def test_pickle():
max_episode_length = 16
env = PointEnv()
policy = FixedPolicy(env.spec,
scripted_actions=[
env.action_space.sample()
for _ in range(max_episode_length)
])
tasks = SetTaskSampler(PointEnv)
n_workers = 8
workers = WorkerFactory(seed=100,
max_episode_length=max_episode_length,
n_workers=n_workers)
sampler = MultiprocessingSampler.from_worker_factory(workers, policy, env)
sampler_pickled = pickle.dumps(sampler)
sampler.shutdown_worker()
sampler2 = pickle.loads(sampler_pickled)
episodes = sampler2.obtain_samples(0,
161,
np.asarray(policy.get_param_values()),
env_update=tasks.sample(n_workers))
mean_rewards = []
goals = []
for eps in episodes.split():
mean_rewards.append(eps.rewards.mean())
goals.append(eps.env_infos['task'][0]['goal'])
assert np.var(mean_rewards) > 0
assert np.var(goals) > 0
sampler2.shutdown_worker()
env.close()
def test_init_without_worker_factory():
max_episode_length = 16
env = PointEnv()
policy = FixedPolicy(env.spec,
scripted_actions=[
env.action_space.sample()
for _ in range(max_episode_length)
])
sampler = MultiprocessingSampler(agents=policy,
envs=env,
seed=100,
max_episode_length=max_episode_length)
worker_factory = WorkerFactory(seed=100,
max_episode_length=max_episode_length)
assert sampler._factory._seed == worker_factory._seed
assert (sampler._factory._max_episode_length ==
worker_factory._max_episode_length)
with pytest.raises(TypeError, match='Must construct a sampler from'):
MultiprocessingSampler(agents=policy, envs=env)
sampler.shutdown_worker()
env.close()
def test_init_with_env_updates():
max_episode_length = 16
env = GarageEnv(PointEnv())
policy = FixedPolicy(env.spec,
scripted_actions=[
env.action_space.sample()
for _ in range(max_episode_length)
])
tasks = SetTaskSampler(lambda: GarageEnv(PointEnv()))
n_workers = 8
workers = WorkerFactory(seed=100,
max_episode_length=max_episode_length,
n_workers=n_workers)
sampler = MultiprocessingSampler.from_worker_factory(
workers, policy, envs=tasks.sample(n_workers))
rollouts = sampler.obtain_samples(0, 160, policy)
assert sum(rollouts.lengths) >= 160
sampler.shutdown_worker()
env.close()
def test_obtain_samples():
env = GarageEnv(GridWorldEnv(desc='4x4'))
policy = ScriptedPolicy(
scripted_actions=[2, 2, 1, 0, 3, 1, 1, 1, 2, 2, 1, 1, 1, 2, 2, 1])
algo = Mock(env_spec=env.spec, policy=policy, max_episode_length=16)
workers = WorkerFactory(seed=100,
max_episode_length=algo.max_episode_length,
n_workers=8)
sampler1 = MultiprocessingSampler.from_worker_factory(workers, policy, env)
sampler2 = LocalSampler.from_worker_factory(workers, policy, env)
trajs1 = sampler1.obtain_samples(0, 1000,
tuple(algo.policy.get_param_values()))
trajs2 = sampler2.obtain_samples(0, 1000,
tuple(algo.policy.get_param_values()))
# pylint: disable=superfluous-parens
assert trajs1.observations.shape[0] >= 1000
assert trajs1.actions.shape[0] >= 1000
assert (sum(trajs1.rewards[:trajs1.lengths[0]]) == sum(
trajs2.rewards[:trajs2.lengths[0]]) == 1)
true_obs = np.array([0, 1, 2, 6, 10, 14])
true_actions = np.array([2, 2, 1, 1, 1, 2])
true_rewards = np.array([0, 0, 0, 0, 0, 1])
start = 0
for length in trajs1.lengths:
observations = trajs1.observations[start:start + length]
actions = trajs1.actions[start:start + length]
rewards = trajs1.rewards[start:start + length]
assert np.array_equal(observations, true_obs)
assert np.array_equal(actions, true_actions)
assert np.array_equal(rewards, true_rewards)
start += length
sampler1.shutdown_worker()
sampler2.shutdown_worker()
env.close()