def test_dm_control_tf_policy(self):
task = ALL_TASKS[0]
with LocalTFRunner(snapshot_config, sess=self.sess) as runner:
env = GarageEnv(DmControlEnv.from_suite(*task))
policy = GaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=(32, 32),
)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(
env_spec=env.spec,
policy=policy,
baseline=baseline,
max_episode_length=5,
discount=0.99,
max_kl_step=0.01,
)
runner.setup(algo, env, sampler_cls=LocalSampler)
runner.train(n_epochs=1, batch_size=10)
env.close()
def test_ppo_pendulum_recurrent_continuous_baseline(self):
"""Test PPO with Pendulum environment and recurrent policy."""
with LocalTFRunner(snapshot_config) as runner:
env = GarageEnv(normalize(gym.make('InvertedDoublePendulum-v2')))
policy = GaussianLSTMPolicy(env_spec=env.spec, )
baseline = ContinuousMLPBaseline(
env_spec=env.spec,
regressor_args=dict(hidden_sizes=(32, 32)),
)
algo = PPO(
env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99,
gae_lambda=0.95,
lr_clip_range=0.2,
optimizer_args=dict(
batch_size=32,
max_epochs=10,
),
stop_entropy_gradient=True,
entropy_method='max',
policy_ent_coeff=0.02,
center_adv=False,
)
runner.setup(algo, env, sampler_cls=LocalSampler)
last_avg_ret = runner.train(n_epochs=10, batch_size=2048)
assert last_avg_ret > 100
env.close()
def test_ddpg_double_pendulum(self):
"""Test DDPG with Pendulum environment."""
with LocalTFRunner(snapshot_config, sess=self.sess) as runner:
env = GarageEnv(gym.make('InvertedDoublePendulum-v2'))
policy = ContinuousMLPPolicy(env_spec=env.spec,
hidden_sizes=[64, 64],
hidden_nonlinearity=tf.nn.relu,
output_nonlinearity=tf.nn.tanh)
exploration_policy = AddOrnsteinUhlenbeckNoise(env.spec,
policy,
sigma=0.2)
qf = ContinuousMLPQFunction(env_spec=env.spec,
hidden_sizes=[64, 64],
hidden_nonlinearity=tf.nn.relu)
replay_buffer = PathBuffer(capacity_in_transitions=int(1e5))
algo = DDPG(
env_spec=env.spec,
policy=policy,
policy_lr=1e-4,
qf_lr=1e-3,
qf=qf,
replay_buffer=replay_buffer,
max_path_length=100,
steps_per_epoch=20,
target_update_tau=1e-2,
n_train_steps=50,
discount=0.9,
min_buffer_size=int(5e3),
exploration_policy=exploration_policy,
)
runner.setup(algo, env)
last_avg_ret = runner.train(n_epochs=10, batch_size=100)
assert last_avg_ret > 60
env.close()
def osimArmResume(ctxt=None,
snapshot_dir='data/local/experiment/osimArm_153',
seed=1):
set_seed(seed)
with LocalTFRunner(snapshot_config=ctxt) as runner:
runner.restore(snapshot_dir)
ddpg = runner._algo
env = GarageEnv(Arm2DVecEnv(visualize=True))
env.reset()
policy = ddpg.policy
env.render()
obs = env.step(env.action_space.sample())
steps = 0
n_steps = 100
while True:
if steps == n_steps:
env.close()
break
temp = policy.get_action(obs[0])
obs = env.step(temp[0])
env.render()
steps += 1
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()
class TestDiscretePolicies(TfGraphTestCase):
def setup_method(self):
super().setup_method()
self.env = GarageEnv(DummyDiscreteEnv())
def teardown_method(self):
self.env.close()
super().teardown_method()
def test_categorial_gru_policy(self):
categorical_gru_policy = CategoricalGRUPolicy(
env_spec=self.env, hidden_dim=1, state_include_action=False)
categorical_gru_policy.reset()
obs = self.env.observation_space.high
assert categorical_gru_policy.get_action(obs)
def test_categorical_lstm_policy(self):
categorical_lstm_policy = CategoricalLSTMPolicy(
env_spec=self.env, hidden_dim=1, state_include_action=False)
categorical_lstm_policy.reset()
obs = self.env.observation_space.high
assert categorical_lstm_policy.get_action(obs)
def test_categorial_mlp_policy(self):
categorical_mlp_policy = CategoricalMLPPolicy(env_spec=self.env,
hidden_sizes=(1, ))
obs = self.env.observation_space.high
assert categorical_mlp_policy.get_action(obs)
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_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_maml_trpo_pendulum():
"""Test PPO with Pendulum environment."""
env = GarageEnv(normalize(HalfCheetahDirEnv(), expected_action_scale=10.))
policy = GaussianMLPPolicy(
env_spec=env.spec,
hidden_sizes=(64, 64),
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None,
)
value_function = GaussianMLPValueFunction(env_spec=env.spec,
hidden_sizes=(32, 32))
rollouts_per_task = 5
max_episode_length = 100
runner = LocalRunner(snapshot_config)
algo = MAMLTRPO(env=env,
policy=policy,
value_function=value_function,
max_episode_length=max_episode_length,
meta_batch_size=5,
discount=0.99,
gae_lambda=1.,
inner_lr=0.1,
num_grad_updates=1)
runner.setup(algo, env, sampler_cls=LocalSampler)
last_avg_ret = runner.train(n_epochs=5,
batch_size=rollouts_per_task *
max_episode_length)
assert last_avg_ret > -5
env.close()
class TestTRPO:
"""Test class for TRPO."""
def setup_method(self):
"""Setup method which is called before every test."""
self.env = GarageEnv(normalize(gym.make('InvertedDoublePendulum-v2')))
self.policy = GaussianMLPPolicy(
env_spec=self.env.spec,
hidden_sizes=(64, 64),
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None,
)
self.value_function = GaussianMLPValueFunction(env_spec=self.env.spec)
def teardown_method(self):
"""Teardown method which is called after every test."""
self.env.close()
@pytest.mark.mujoco
def test_trpo_pendulum(self):
"""Test TRPO with Pendulum environment."""
deterministic.set_seed(0)
runner = LocalRunner(snapshot_config)
algo = TRPO(env_spec=self.env.spec,
policy=self.policy,
value_function=self.value_function,
max_episode_length=100,
discount=0.99,
gae_lambda=0.98)
runner.setup(algo, self.env, sampler_cls=LocalSampler)
last_avg_ret = runner.train(n_epochs=10, batch_size=100)
assert last_avg_ret > 0
def test_trpo_cnn_cubecrash(self):
with LocalTFRunner(snapshot_config, sess=self.sess) as runner:
env = GarageEnv(normalize(gym.make('CubeCrash-v0')))
policy = CategoricalCNNPolicy(env_spec=env.spec,
filters=((32, (8, 8)), (64, (4, 4))),
strides=(4, 2),
padding='VALID',
hidden_sizes=(32, 32))
baseline = GaussianCNNBaseline(
env_spec=env.spec,
regressor_args=dict(filters=((32, (8, 8)), (64, (4, 4))),
strides=(4, 2),
padding='VALID',
hidden_sizes=(32, 32),
use_trust_region=True))
algo = TRPO(env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99,
gae_lambda=0.98,
max_kl_step=0.01,
policy_ent_coeff=0.0,
flatten_input=False)
runner.setup(algo, env)
last_avg_ret = runner.train(n_epochs=10, batch_size=2048)
assert last_avg_ret > -1.5
env.close()
class TestContinuousPolicies(TfGraphTestCase):
def setup_method(self):
super().setup_method()
self.env = GarageEnv(DummyBoxEnv())
self.obs_var = tf.compat.v1.placeholder(
tf.float32,
shape=[None, None, self.env.observation_space.flat_dim],
name='obs')
def teardown_method(self):
self.env.close()
super().teardown_method()
def test_continuous_mlp_policy(self):
continuous_mlp_policy = ContinuousMLPPolicy(env_spec=self.env,
hidden_sizes=(1, ))
self.sess.run(tf.compat.v1.global_variables_initializer())
obs = self.env.observation_space.high
assert continuous_mlp_policy.get_action(obs)
def test_gaussian_gru_policy(self):
gaussian_gru_policy = GaussianGRUPolicy(env_spec=self.env,
hidden_dim=1,
state_include_action=False)
self.sess.run(tf.compat.v1.global_variables_initializer())
gaussian_gru_policy.build(self.obs_var)
gaussian_gru_policy.reset()
obs = self.env.observation_space.high
assert gaussian_gru_policy.get_action(obs)
def test_gaussian_lstm_policy(self):
gaussian_lstm_policy = GaussianLSTMPolicy(env_spec=self.env,
hidden_dim=1,
state_include_action=False)
self.sess.run(tf.compat.v1.global_variables_initializer())
gaussian_lstm_policy.build(self.obs_var)
gaussian_lstm_policy.reset()
obs = self.env.observation_space.high
assert gaussian_lstm_policy.get_action(obs)
def test_gaussian_mlp_policy(self):
gaussian_mlp_policy = GaussianMLPPolicy(env_spec=self.env,
hidden_sizes=(1, ))
self.sess.run(tf.compat.v1.global_variables_initializer())
gaussian_mlp_policy.build(self.obs_var)
obs = self.env.observation_space.high
assert gaussian_mlp_policy.get_action(obs)
def test_baseline(self):
"""Test the baseline initialization."""
box_env = GarageEnv(DummyBoxEnv())
deterministic_mlp_baseline = ContinuousMLPBaseline(env_spec=box_env)
gaussian_mlp_baseline = GaussianMLPBaseline(env_spec=box_env)
self.sess.run(tf.compat.v1.global_variables_initializer())
deterministic_mlp_baseline.get_param_values()
gaussian_mlp_baseline.get_param_values()
box_env.close()
class TestMAMLVPG:
"""Test class for MAML-VPG."""
def setup_method(self):
"""Setup method which is called before every test."""
self.env = GarageEnv(
normalize(HalfCheetahDirEnv(), expected_action_scale=10.))
self.policy = GaussianMLPPolicy(
env_spec=self.env.spec,
hidden_sizes=(64, 64),
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None,
)
self.value_function = GaussianMLPValueFunction(env_spec=self.env.spec,
hidden_sizes=(32, 32))
def teardown_method(self):
"""Teardown method which is called after every test."""
self.env.close()
def test_ppo_pendulum(self):
"""Test PPO with Pendulum environment."""
deterministic.set_seed(0)
rollouts_per_task = 5
max_path_length = 100
task_sampler = SetTaskSampler(lambda: GarageEnv(
normalize(HalfCheetahDirEnv(), expected_action_scale=10.)))
meta_evaluator = MetaEvaluator(test_task_sampler=task_sampler,
max_path_length=max_path_length,
n_test_tasks=1,
n_test_rollouts=10)
runner = LocalRunner(snapshot_config)
algo = MAMLVPG(env=self.env,
policy=self.policy,
value_function=self.value_function,
max_path_length=max_path_length,
meta_batch_size=5,
discount=0.99,
gae_lambda=1.,
inner_lr=0.1,
num_grad_updates=1,
meta_evaluator=meta_evaluator)
runner.setup(algo, self.env)
last_avg_ret = runner.train(n_epochs=10,
batch_size=rollouts_per_task *
max_path_length)
assert last_avg_ret > -5
def test_dqn_cartpole_pickle(self):
"""Test DQN with CartPole environment."""
deterministic.set_seed(100)
with LocalTFRunner(snapshot_config, sess=self.sess) as runner:
n_epochs = 10
steps_per_epoch = 10
sampler_batch_size = 500
num_timesteps = n_epochs * steps_per_epoch * sampler_batch_size
env = GarageEnv(gym.make('CartPole-v0'))
replay_buffer = PathBuffer(capacity_in_transitions=int(1e4))
qf = DiscreteMLPQFunction(env_spec=env.spec, hidden_sizes=(64, 64))
policy = DiscreteQfDerivedPolicy(env_spec=env.spec, qf=qf)
epilson_greedy_policy = EpsilonGreedyPolicy(
env_spec=env.spec,
policy=policy,
total_timesteps=num_timesteps,
max_epsilon=1.0,
min_epsilon=0.02,
decay_ratio=0.1)
algo = DQN(env_spec=env.spec,
policy=policy,
qf=qf,
exploration_policy=epilson_greedy_policy,
replay_buffer=replay_buffer,
max_path_length=100,
qf_lr=1e-4,
discount=1.0,
min_buffer_size=int(1e3),
double_q=False,
n_train_steps=500,
grad_norm_clipping=5.0,
steps_per_epoch=steps_per_epoch,
target_network_update_freq=1,
buffer_batch_size=32)
runner.setup(algo, env)
with tf.compat.v1.variable_scope(
'DiscreteMLPQFunction/mlp/hidden_0', reuse=True):
bias = tf.compat.v1.get_variable('bias')
# assign it to all one
old_bias = tf.ones_like(bias).eval()
bias.load(old_bias)
h = pickle.dumps(algo)
with tf.compat.v1.Session(graph=tf.Graph()):
pickle.loads(h)
with tf.compat.v1.variable_scope(
'DiscreteMLPQFunction/mlp/hidden_0', reuse=True):
new_bias = tf.compat.v1.get_variable('bias')
new_bias = new_bias.eval()
assert np.array_equal(old_bias, new_bias)
env.close()
def test_polopt_algo(self, algo_cls, env_cls, policy_cls, baseline_cls):
print("Testing %s, %s, %s" %
(algo_cls.__name__, env_cls.__name__, policy_cls.__name__))
env = GarageEnv(env_cls())
policy = policy_cls(env_spec=env)
baseline = baseline_cls(env_spec=env)
algo = algo_cls(env=env,
policy=policy,
baseline=baseline,
**(algo_args.get(algo_cls, dict())))
algo.train()
assert not np.any(np.isnan(policy.get_param_values()))
env.close()
def stest_cma_es_cartpole():
"""Test CMAES with Cartpole-v1 environment."""
with LocalTFRunner(snapshot_config) as runner:
with TensorBoardPytorchWriter(PROJECT_APP_PATH.user_log /
"CMA") as writer:
env = GarageEnv(env_name="CartPole-v1")
algo = CovarianceMatrixAdaptationEvolutionStrategyAgent(
env_spec=env.spec, max_rollout_length=100)
algo.build()
runner.setup(algo, env, sampler_cls=LocalSampler)
runner.train(n_epochs=1, batch_size=1000)
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_ddpg_pendulum(self):
"""Test DDPG with Pendulum environment.
This environment has a [-3, 3] action_space bound.
"""
deterministic.set_seed(0)
runner = LocalRunner(snapshot_config)
env = GarageEnv(normalize(gym.make('InvertedPendulum-v2')))
policy = DeterministicMLPPolicy(env_spec=env.spec,
hidden_sizes=[64, 64],
hidden_nonlinearity=F.relu,
output_nonlinearity=torch.tanh)
exploration_policy = AddOrnsteinUhlenbeckNoise(env.spec,
policy,
sigma=0.2)
qf = ContinuousMLPQFunction(env_spec=env.spec,
hidden_sizes=[64, 64],
hidden_nonlinearity=F.relu)
replay_buffer = SimpleReplayBuffer(env_spec=env.spec,
size_in_transitions=int(1e6),
time_horizon=100)
algo = DDPG(env_spec=env.spec,
policy=policy,
qf=qf,
replay_buffer=replay_buffer,
steps_per_epoch=20,
n_train_steps=50,
min_buffer_size=int(1e4),
exploration_policy=exploration_policy,
target_update_tau=1e-2,
discount=0.9)
runner.setup(algo, env)
last_avg_ret = runner.train(n_epochs=10, batch_size=100)
assert last_avg_ret > 10
env.close()
class TestRaySamplerTF():
"""
Uses mock policy for 4x4 gridworldenv
'4x4': [
'SFFF',
'FHFH',
'FFFH',
'HFFG'
]
0: left
1: down
2: right
3: up
-1: no move
'S' : starting point
'F' or '.': free space
'W' or 'x': wall
'H' or 'o': hole (terminates episode)
'G' : goal
[2,2,1,0,3,1,1,1,2,2,1,1,1,2,2,1]
"""
def setup_method(self):
self.env = GarageEnv(GridWorldEnv(desc='4x4'))
self.policy = ScriptedPolicy(
scripted_actions=[2, 2, 1, 0, 3, 1, 1, 1, 2, 2, 1, 1, 1, 2, 2, 1])
self.algo = Mock(env_spec=self.env.spec,
policy=self.policy,
max_path_length=16)
def teardown_method(self):
self.env.close()
def test_ray_batch_sampler(self, ray_local_session_fixture):
del ray_local_session_fixture
assert ray.is_initialized()
workers = WorkerFactory(seed=100,
max_path_length=self.algo.max_path_length)
sampler1 = RaySampler(workers, self.policy, self.env)
sampler1.start_worker()
sampler1.shutdown_worker()
def test_dqn_cartpole_grad_clip(self):
"""Test DQN with CartPole environment."""
deterministic.set_seed(100)
with LocalTFRunner(snapshot_config, sess=self.sess) as runner:
n_epochs = 10
steps_per_epoch = 10
sampler_batch_size = 500
num_timesteps = n_epochs * steps_per_epoch * sampler_batch_size
env = GarageEnv(gym.make('CartPole-v0'))
replay_buffer = PathBuffer(capacity_in_transitions=int(1e4))
qf = DiscreteMLPQFunction(env_spec=env.spec, hidden_sizes=(64, 64))
policy = DiscreteQfDerivedPolicy(env_spec=env.spec, qf=qf)
epilson_greedy_policy = EpsilonGreedyPolicy(
env_spec=env.spec,
policy=policy,
total_timesteps=num_timesteps,
max_epsilon=1.0,
min_epsilon=0.02,
decay_ratio=0.1)
algo = DQN(env_spec=env.spec,
policy=policy,
qf=qf,
exploration_policy=epilson_greedy_policy,
replay_buffer=replay_buffer,
max_path_length=100,
qf_lr=1e-4,
discount=1.0,
min_buffer_size=int(1e3),
double_q=False,
n_train_steps=500,
grad_norm_clipping=5.0,
steps_per_epoch=steps_per_epoch,
target_network_update_freq=1,
buffer_batch_size=32)
runner.setup(algo, env)
last_avg_ret = runner.train(n_epochs=n_epochs,
batch_size=sampler_batch_size)
assert last_avg_ret > 9
env.close()
class TestNormalizedGym:
def setup_method(self):
self.env = GarageEnv(
normalize(gym.make('Pendulum-v0'),
normalize_reward=True,
normalize_obs=True,
flatten_obs=True))
def teardown_method(self):
self.env.close()
def test_does_not_modify_action(self):
a = self.env.action_space.sample()
a_copy = a
self.env.reset()
self.env.step(a)
assert a == a_copy
def test_flatten(self):
for _ in range(10):
self.env.reset()
for _ in range(5):
self.env.render()
action = self.env.action_space.sample()
next_obs, _, done, _ = self.env.step(action)
assert next_obs.shape == self.env.observation_space.low.shape
if done:
break
def test_unflatten(self):
for _ in range(10):
self.env.reset()
for _ in range(5):
action = self.env.action_space.sample()
next_obs, _, done, _ = self.env.step(action)
# yapf: disable
assert (self.env.observation_space.flatten(next_obs).shape
== self.env.observation_space.flat_dim)
# yapf: enable
if done:
break
class TestContinuousPolicies(TfGraphTestCase):
def setup_method(self):
super().setup_method()
self.env = GarageEnv(DummyBoxEnv())
def teardown_method(self):
self.env.close()
super().teardown_method()
def test_continuous_mlp_policy(self):
continuous_mlp_policy = ContinuousMLPPolicy(env_spec=self.env,
hidden_sizes=(1, ))
obs = self.env.observation_space.high
assert continuous_mlp_policy.get_action(obs)
def test_gaussian_gru_policy(self):
gaussian_gru_policy = GaussianGRUPolicy(env_spec=self.env,
hidden_dim=1,
state_include_action=False)
gaussian_gru_policy.reset()
obs = self.env.observation_space.high
assert gaussian_gru_policy.get_action(obs)
def test_gaussian_lstm_policy(self):
gaussian_lstm_policy = GaussianLSTMPolicy(env_spec=self.env,
hidden_dim=1,
state_include_action=False)
gaussian_lstm_policy.reset()
obs = self.env.observation_space.high
assert gaussian_lstm_policy.get_action(obs)
def test_gaussian_mlp_policy(self):
gaussian_mlp_policy = GaussianMLPPolicy(env_spec=self.env,
hidden_sizes=(1, ))
obs = self.env.observation_space.high
assert gaussian_mlp_policy.get_action(obs)
def test_reps_cartpole(self):
"""Test REPS with gym Cartpole environment."""
with LocalTFRunner(snapshot_config, sess=self.sess) as runner:
env = GarageEnv(gym.make('CartPole-v0'))
policy = CategoricalMLPPolicy(env_spec=env.spec,
hidden_sizes=[32, 32])
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = REPS(env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99)
runner.setup(algo, env)
last_avg_ret = runner.train(n_epochs=10, batch_size=4000)
assert last_avg_ret > 5
env.close()
def test_obtain_samples(ray_local_session_fixture):
del ray_local_session_fixture
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_path_length=16)
assert ray.is_initialized()
workers = WorkerFactory(seed=100,
max_path_length=algo.max_path_length,
n_workers=8)
sampler1 = RaySampler.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()))
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()
def test_gaussian_policies(self, policy_cls):
with LocalTFRunner(snapshot_config, sess=self.sess) as runner:
env = GarageEnv(normalize(gym.make('Pendulum-v0')))
policy = policy_cls(name='policy', env_spec=env.spec)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(
env_spec=env.spec,
policy=policy,
baseline=baseline,
max_episode_length=100,
discount=0.99,
max_kl_step=0.01,
optimizer=ConjugateGradientOptimizer,
optimizer_args=dict(hvp_approach=FiniteDifferenceHvp(
base_eps=1e-5)),
)
runner.setup(algo, env, sampler_cls=LocalSampler)
runner.train(n_epochs=1, batch_size=4000)
env.close()
def test_cma_es_cartpole(self):
"""Test CMAES with Cartpole-v1 environment."""
with LocalTFRunner(snapshot_config) as runner:
env = GarageEnv(env_name='CartPole-v1')
policy = CategoricalMLPPolicy(name='policy',
env_spec=env.spec,
hidden_sizes=(32, 32))
baseline = LinearFeatureBaseline(env_spec=env.spec)
n_samples = 20
algo = CMAES(env_spec=env.spec,
policy=policy,
baseline=baseline,
max_episode_length=100,
n_samples=n_samples)
runner.setup(algo, env, sampler_cls=LocalSampler)
runner.train(n_epochs=1, batch_size=1000)
# No assertion on return because CMAES is not stable.
env.close()
def test_on_policy_vectorized_sampler_n_envs(self, cpus, n_envs,
expected_n_envs):
with LocalTFRunner(snapshot_config, sess=self.sess,
max_cpus=cpus) as runner:
env = GarageEnv(gym.make('CartPole-v0'))
policy = CategoricalMLPPolicy(env_spec=env.spec,
hidden_sizes=[32, 32])
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = REPS(env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99)
runner.setup(algo, env, sampler_args=dict(n_envs=n_envs))
assert isinstance(runner._sampler, OnPolicyVectorizedSampler)
assert runner._sampler._n_envs == expected_n_envs
env.close()
def test_trpo_lstm_cartpole(self):
with LocalTFRunner(snapshot_config, sess=self.sess) as runner:
env = GarageEnv(normalize(gym.make('CartPole-v1')))
policy = CategoricalLSTMPolicy(name='policy', env_spec=env.spec)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = TRPO(env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99,
max_kl_step=0.01,
optimizer_args=dict(hvp_approach=FiniteDifferenceHvp(
base_eps=1e-5)))
snapshotter.snapshot_dir = './'
runner.setup(algo, env)
last_avg_ret = runner.train(n_epochs=10, batch_size=2048)
assert last_avg_ret > 80
env.close()
def test_erwr_cartpole(self):
"""Test ERWR with Cartpole-v1 environment."""
with LocalTFRunner(snapshot_config, sess=self.sess) as runner:
env = GarageEnv(env_name='CartPole-v1')
policy = CategoricalMLPPolicy(name='policy',
env_spec=env.spec,
hidden_sizes=(32, 32))
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = ERWR(env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=100,
discount=0.99)
runner.setup(algo, env)
last_avg_ret = runner.train(n_epochs=10, batch_size=10000)
assert last_avg_ret > 80
env.close()
