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_update_envs_env_update(ray_local_session_fixture):
del ray_local_session_fixture
assert ray.is_initialized()
max_path_length = 16
env = GarageEnv(PointEnv())
policy = FixedPolicy(env.spec,
scripted_actions=[
env.action_space.sample()
for _ in range(max_path_length)
])
tasks = SetTaskSampler(PointEnv)
n_workers = 8
workers = WorkerFactory(seed=100,
max_path_length=max_path_length,
n_workers=n_workers)
sampler = RaySampler.from_worker_factory(workers, policy, env)
rollouts = sampler.obtain_samples(0,
160,
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))
def test_update_envs_env_update():
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 = LocalSampler.from_worker_factory(workers, policy, env)
episodes = sampler.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 len(mean_rewards) == 11
assert len(goals) == 11
assert np.var(mean_rewards) > 1e-2
assert np.var(goals) > 1e-2
with pytest.raises(ValueError):
sampler.obtain_samples(0,
10,
np.asarray(policy.get_param_values()),
env_update=tasks.sample(n_workers + 1))
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_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_update_envs_env_update(timesteps_per_call):
max_episode_length = 16
env = PointEnv()
n_workers = 8
policies = [
FixedPolicy(env.spec,
scripted_actions=[
env.action_space.sample()
for _ in range(max_episode_length)
]) for _ in range(n_workers)
]
tasks = SetTaskSampler(PointEnv)
workers = WorkerFactory(seed=100,
max_episode_length=max_episode_length,
n_workers=n_workers,
worker_class=FragmentWorker,
worker_args=dict(
n_envs=1,
timesteps_per_call=timesteps_per_call))
sampler = LocalSampler.from_worker_factory(workers, policies, env)
episodes = sampler.obtain_samples(0,
160,
None,
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 len(mean_rewards) == int(160 / timesteps_per_call)
assert len(goals) == int(160 / timesteps_per_call)
assert np.var(mean_rewards) > 1e-2
assert np.var(goals) > 1e-2
with pytest.raises(ValueError):
sampler.obtain_samples(0,
10,
None,
env_update=tasks.sample(n_workers + 1))
def test_init_with_env_updates():
max_path_length = 16
env = TfEnv(PointEnv())
policy = FixedPolicy(env.spec,
scripted_actions=[
env.action_space.sample()
for _ in range(max_path_length)
])
tasks = SetTaskSampler(lambda: TfEnv(PointEnv()))
n_workers = 8
workers = WorkerFactory(seed=100,
max_path_length=max_path_length,
n_workers=n_workers)
sampler = RaySampler.from_worker_factory(workers,
policy,
envs=tasks.sample(n_workers))
rollouts = sampler.obtain_samples(0, 160, policy)
assert sum(rollouts.lengths) >= 160
def test_init_with_env_updates():
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 = LocalSampler.from_worker_factory(workers,
policy,
envs=tasks.sample(n_workers))
episodes = sampler.obtain_samples(0, 160, policy)
assert sum(episodes.lengths) >= 160
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 load_pearl(env_name="CartPole-v0"):
"""Return an instance of the PEARL algorithm.
NOTE: currently not working.
"""
num_train_tasks = 100
num_test_tasks = 30
latent_size = 5
net_size = 300
encoder_hidden_size = 200
encoder_hidden_sizes = (encoder_hidden_size, encoder_hidden_size,
encoder_hidden_size)
# Create multi-task environment and sample tasks.
env_start = GarageEnv(env_name=env_name)
env_sampler = SetTaskSampler(lambda: GarageEnv(normalize(env_start)))
env = env_sampler.sample(num_train_tasks)
test_env_sampler = SetTaskSampler(lambda: GarageEnv(normalize(env_start)))
# Instantiate networks.
augmented_env = PEARL.augment_env_spec(env[0](), latent_size)
qf = ContinuousMLPQFunction(env_spec=augmented_env,
hidden_sizes=[net_size, net_size, net_size])
vf_env = PEARL.get_env_spec(env[0](), latent_size, 'vf')
vf = ContinuousMLPQFunction(env_spec=vf_env,
hidden_sizes=[net_size, net_size, net_size])
inner_policy = TanhGaussianMLPPolicy(
env_spec=augmented_env, hidden_sizes=[net_size, net_size, net_size])
pearl = PEARL(env=env,
inner_policy=inner_policy,
qf=qf,
vf=vf,
num_train_tasks=num_train_tasks,
num_test_tasks=num_test_tasks,
latent_dim=latent_size,
encoder_hidden_sizes=encoder_hidden_sizes,
test_env_sampler=test_env_sampler)
return pearl
def test_init_with_env_updates(ray_local_session_fixture):
del ray_local_session_fixture
assert ray.is_initialized()
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 = RaySampler.from_worker_factory(workers,
policy,
envs=tasks.sample(n_workers))
rollouts = sampler.obtain_samples(0, 160, policy)
assert sum(rollouts.lengths) >= 160
def test_pickling(self):
"""Test pickle and unpickle."""
net_size = 10
env_sampler = SetTaskSampler(PointEnv)
env = env_sampler.sample(5)
test_env_sampler = SetTaskSampler(PointEnv)
augmented_env = PEARL.augment_env_spec(env[0](), 5)
qf = ContinuousMLPQFunction(
env_spec=augmented_env,
hidden_sizes=[net_size, net_size, net_size])
vf_env = PEARL.get_env_spec(env[0](), 5, 'vf')
vf = ContinuousMLPQFunction(
env_spec=vf_env, hidden_sizes=[net_size, net_size, net_size])
inner_policy = TanhGaussianMLPPolicy(
env_spec=augmented_env,
hidden_sizes=[net_size, net_size, net_size])
pearl = PEARL(env=env,
inner_policy=inner_policy,
qf=qf,
vf=vf,
num_train_tasks=5,
num_test_tasks=5,
latent_dim=5,
encoder_hidden_sizes=[10, 10],
test_env_sampler=test_env_sampler)
# This line is just to improve coverage
pearl.to()
pickled = pickle.dumps(pearl)
unpickled = pickle.loads(pickled)
assert hasattr(unpickled, '_replay_buffers')
assert hasattr(unpickled, '_context_replay_buffers')
assert unpickled._is_resuming
def test_pearl_ml1_push(self):
"""Test PEARL with ML1 Push environment."""
params = dict(seed=1,
num_epochs=1,
num_train_tasks=5,
latent_size=7,
encoder_hidden_sizes=[10, 10, 10],
net_size=30,
meta_batch_size=16,
num_steps_per_epoch=40,
num_initial_steps=40,
num_tasks_sample=15,
num_steps_prior=15,
num_extra_rl_steps_posterior=15,
batch_size=256,
embedding_batch_size=8,
embedding_mini_batch_size=8,
reward_scale=10.,
use_information_bottleneck=True,
use_next_obs_in_context=False,
use_gpu=False)
net_size = params['net_size']
set_seed(params['seed'])
# create multi-task environment and sample tasks
ml1 = metaworld.ML1('push-v1')
train_env = MetaWorldSetTaskEnv(ml1, 'train')
env_sampler = SetTaskSampler(MetaWorldSetTaskEnv,
env=train_env,
wrapper=lambda env, _: normalize(env))
env = env_sampler.sample(params['num_train_tasks'])
test_env = MetaWorldSetTaskEnv(ml1, 'test')
test_env_sampler = SetTaskSampler(
MetaWorldSetTaskEnv,
env=test_env,
wrapper=lambda env, _: normalize(env))
augmented_env = PEARL.augment_env_spec(env[0](), params['latent_size'])
qf = ContinuousMLPQFunction(
env_spec=augmented_env,
hidden_sizes=[net_size, net_size, net_size])
vf_env = PEARL.get_env_spec(env[0](), params['latent_size'], 'vf')
vf = ContinuousMLPQFunction(
env_spec=vf_env, hidden_sizes=[net_size, net_size, net_size])
inner_policy = TanhGaussianMLPPolicy(
env_spec=augmented_env,
hidden_sizes=[net_size, net_size, net_size])
pearl = PEARL(
env=env,
policy_class=ContextConditionedPolicy,
encoder_class=MLPEncoder,
inner_policy=inner_policy,
qf=qf,
vf=vf,
num_train_tasks=params['num_train_tasks'],
latent_dim=params['latent_size'],
encoder_hidden_sizes=params['encoder_hidden_sizes'],
test_env_sampler=test_env_sampler,
meta_batch_size=params['meta_batch_size'],
num_steps_per_epoch=params['num_steps_per_epoch'],
num_initial_steps=params['num_initial_steps'],
num_tasks_sample=params['num_tasks_sample'],
num_steps_prior=params['num_steps_prior'],
num_extra_rl_steps_posterior=params[
'num_extra_rl_steps_posterior'],
batch_size=params['batch_size'],
embedding_batch_size=params['embedding_batch_size'],
embedding_mini_batch_size=params['embedding_mini_batch_size'],
reward_scale=params['reward_scale'],
)
set_gpu_mode(params['use_gpu'], gpu_id=0)
if params['use_gpu']:
pearl.to()
trainer = Trainer(snapshot_config)
trainer.setup(algo=pearl,
env=env[0](),
sampler_cls=LocalSampler,
n_workers=1,
worker_class=PEARLWorker)
trainer.train(n_epochs=params['num_epochs'],
batch_size=params['batch_size'])
def pearl_metaworld_ml10(ctxt=None,
seed=1,
num_epochs=1000,
num_train_tasks=10,
latent_size=7,
encoder_hidden_size=200,
net_size=300,
meta_batch_size=16,
num_steps_per_epoch=4000,
num_initial_steps=4000,
num_tasks_sample=15,
num_steps_prior=750,
num_extra_rl_steps_posterior=750,
batch_size=256,
embedding_batch_size=64,
embedding_mini_batch_size=64,
reward_scale=10.,
use_gpu=False):
"""Train PEARL with ML10 environments.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the snapshotter.
seed (int): Used to seed the random number generator to produce
determinism.
num_epochs (int): Number of training epochs.
num_train_tasks (int): Number of tasks for training.
latent_size (int): Size of latent context vector.
encoder_hidden_size (int): Output dimension of dense layer of the
context encoder.
net_size (int): Output dimension of a dense layer of Q-function and
value function.
meta_batch_size (int): Meta batch size.
num_steps_per_epoch (int): Number of iterations per epoch.
num_initial_steps (int): Number of transitions obtained per task before
training.
num_tasks_sample (int): Number of random tasks to obtain data for each
iteration.
num_steps_prior (int): Number of transitions to obtain per task with
z ~ prior.
num_extra_rl_steps_posterior (int): Number of additional transitions
to obtain per task with z ~ posterior that are only used to train
the policy and NOT the encoder.
batch_size (int): Number of transitions in RL batch.
embedding_batch_size (int): Number of transitions in context batch.
embedding_mini_batch_size (int): Number of transitions in mini context
batch; should be same as embedding_batch_size for non-recurrent
encoder.
reward_scale (int): Reward scale.
use_gpu (bool): Whether or not to use GPU for training.
"""
set_seed(seed)
encoder_hidden_sizes = (encoder_hidden_size, encoder_hidden_size,
encoder_hidden_size)
ml10 = metaworld.ML10()
train_env = MetaWorldSetTaskEnv(ml10, 'train')
env_sampler = SetTaskSampler(MetaWorldSetTaskEnv,
env=train_env,
wrapper=lambda env, _: normalize(env))
env = env_sampler.sample(num_train_tasks)
test_env = MetaWorldSetTaskEnv(ml10, 'test')
test_env_sampler = SetTaskSampler(MetaWorldSetTaskEnv,
env=test_env,
wrapper=lambda env, _: normalize(env))
trainer = Trainer(ctxt)
# instantiate networks
augmented_env = PEARL.augment_env_spec(env[0](), latent_size)
qf = ContinuousMLPQFunction(env_spec=augmented_env,
hidden_sizes=[net_size, net_size, net_size])
vf_env = PEARL.get_env_spec(env[0](), latent_size, 'vf')
vf = ContinuousMLPQFunction(env_spec=vf_env,
hidden_sizes=[net_size, net_size, net_size])
inner_policy = TanhGaussianMLPPolicy(
env_spec=augmented_env, hidden_sizes=[net_size, net_size, net_size])
pearl = PEARL(
env=env,
policy_class=ContextConditionedPolicy,
encoder_class=MLPEncoder,
inner_policy=inner_policy,
qf=qf,
vf=vf,
num_train_tasks=num_train_tasks,
latent_dim=latent_size,
encoder_hidden_sizes=encoder_hidden_sizes,
test_env_sampler=test_env_sampler,
meta_batch_size=meta_batch_size,
num_steps_per_epoch=num_steps_per_epoch,
num_initial_steps=num_initial_steps,
num_tasks_sample=num_tasks_sample,
num_steps_prior=num_steps_prior,
num_extra_rl_steps_posterior=num_extra_rl_steps_posterior,
batch_size=batch_size,
embedding_batch_size=embedding_batch_size,
embedding_mini_batch_size=embedding_mini_batch_size,
reward_scale=reward_scale,
)
set_gpu_mode(use_gpu, gpu_id=0)
if use_gpu:
pearl.to()
trainer.setup(algo=pearl,
env=env[0](),
sampler_cls=LocalSampler,
n_workers=1,
worker_class=PEARLWorker)
trainer.train(n_epochs=num_epochs, batch_size=batch_size)
def tcl_pearl_ml1(ctxt=None,
seed=1,
num_epochs=200,
num_train_tasks=50,
num_test_tasks=10,
latent_size=7,
encoder_hidden_size=200,
net_size=300,
meta_batch_size=16,
num_steps_per_epoch=4000,
num_initial_steps=4000,
num_tasks_sample=15,
num_steps_prior=750,
num_extra_rl_steps_posterior=750,
batch_size=256,
embedding_batch_size=64,
embedding_mini_batch_size=64,
max_path_length=200,
reward_scale=10.,
replay_buffer_size=1000000,
use_next_obs=False,
in_sequence_path_aug=True,
emphasized_network=False,
use_kl_loss=True,
use_q_loss=True,
encoder_common_net=True,
single_alpha=False,
use_task_index_label=False,
use_wasserstein_distance=True,
gpu_id=0,
name='push-v1',
prefix='curl_fine_tune',
use_gpu=True):
"""Train TCL-PEARL with ML1 environments.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by LocalRunner to create the snapshotter.
seed (int): Used to seed the random number generator to produce
determinism.
num_epochs (int): Number of training epochs.
num_train_tasks (int): Number of tasks for training.
num_test_tasks (int): Number of tasks for testing.
latent_size (int): Size of latent context vector.
encoder_hidden_size (int): Output dimension of dense layer of the
context encoder.
net_size (int): Output dimension of a dense layer of Q-function and
value function.
meta_batch_size (int): Meta batch size.
num_steps_per_epoch (int): Number of iterations per epoch.
num_initial_steps (int): Number of transitions obtained per task before
training.
num_tasks_sample (int): Number of random tasks to obtain data for each
iteration.
num_steps_prior (int): Number of transitions to obtain per task with
z ~ prior.
num_extra_rl_steps_posterior (int): Number of additional transitions
to obtain per task with z ~ posterior that are only used to train
the policy and NOT the encoder.
batch_size (int): Number of transitions in RL batch.
embedding_batch_size (int): Number of transitions in context batch.
embedding_mini_batch_size (int): Number of transitions in mini context
batch; should be same as embedding_batch_size for non-recurrent
encoder.
max_path_length (int): Maximum path length.
reward_scale (int): Reward scale.
use_gpu (bool): Whether or not to use GPU for training.
"""
set_seed(seed)
encoder_hidden_sizes = (encoder_hidden_size, encoder_hidden_size,
encoder_hidden_size)
print("Running experiences on {}/{}".format(prefix, name))
# create multi-task environment and sample tasks
ml1 = metaworld.ML1(name)
train_env = MetaWorldSetTaskEnv(ml1, 'train')
env_sampler = SetTaskSampler(MetaWorldSetTaskEnv,
env=train_env,
wrapper=lambda env, _: normalize(env))
env = env_sampler.sample(num_train_tasks)
test_env = MetaWorldSetTaskEnv(ml1, 'test')
test_env_sampler = SetTaskSampler(MetaWorldSetTaskEnv,
env=test_env,
wrapper=lambda env, _: normalize(env))
sampler = LocalSampler(agents=None,
envs=env[0](),
max_episode_length=max_path_length,
n_workers=1,
worker_class=TCLPEARLWorker)
trainer = Trainer(ctxt)
# instantiate networks
augmented_env = TCLPEARL.augment_env_spec(env[0](), latent_size)
qf_1 = ContinuousMLPQFunction(env_spec=augmented_env,
hidden_sizes=[net_size, net_size, net_size])
qf_2 = ContinuousMLPQFunction(env_spec=augmented_env,
hidden_sizes=[net_size, net_size, net_size])
inner_policy = TanhGaussianMLPPolicy(
env_spec=augmented_env, hidden_sizes=[net_size, net_size, net_size])
tcl_pearl = TCLPEARL(
env=env,
policy_class=TCLPolicy,
encoder_class=ContrastiveEncoder,
inner_policy=inner_policy,
qf1=qf_1,
qf2=qf_2,
sampler=sampler,
num_train_tasks=num_train_tasks,
num_test_tasks=num_test_tasks,
latent_dim=latent_size,
encoder_hidden_sizes=encoder_hidden_sizes,
test_env_sampler=test_env_sampler,
meta_batch_size=meta_batch_size,
num_steps_per_epoch=num_steps_per_epoch,
num_initial_steps=num_initial_steps,
num_tasks_sample=num_tasks_sample,
num_steps_prior=num_steps_prior,
num_extra_rl_steps_posterior=num_extra_rl_steps_posterior,
batch_size=batch_size,
embedding_batch_size=embedding_batch_size,
embedding_mini_batch_size=embedding_mini_batch_size,
max_path_length=max_path_length,
reward_scale=reward_scale,
replay_buffer_size=replay_buffer_size,
use_next_obs_in_context=use_next_obs,
embedding_batch_in_sequence=in_sequence_path_aug,
use_kl_loss=use_kl_loss,
use_q_loss=use_q_loss,
encoder_common_net=encoder_common_net,
single_alpha=single_alpha,
use_task_index_label=use_task_index_label,
use_wasserstein_distance=use_wasserstein_distance)
set_gpu_mode(use_gpu, gpu_id=gpu_id)
if use_gpu:
tcl_pearl.to()
trainer.setup(algo=tcl_pearl, env=env[0]())
trainer.train(n_epochs=num_epochs, batch_size=batch_size)
def pearl_half_cheetah(
ctxt=None,
seed=1,
num_epochs=param_num_epoches,
num_train_tasks=param_train_tasks_num,
num_test_tasks=param_test_tasks_num,
latent_size=param_latent_size,
encoder_hidden_size=param_encoder_hidden_size,
net_size=param_net_size,
meta_batch_size=param_meta_batch_size,
num_steps_per_epoch=param_num_steps_per_epoch,
num_initial_steps=param_num_initial_steps,
num_tasks_sample=param_num_tasks_sample,
num_steps_prior=param_num_steps_prior,
num_extra_rl_steps_posterior=param_num_extra_rl_steps_posterior,
batch_size=param_batch_size,
embedding_batch_size=param_embedding_batch_size,
embedding_mini_batch_size=param_embedding_mini_batch_size,
max_path_length=param_max_path_length,
reward_scale=param_reward_scale,
use_gpu=param_use_gpu):
set_seed(seed)
encoder_hidden_sizes = (encoder_hidden_size, encoder_hidden_size,
encoder_hidden_size)
# create multi-task environment and sample tasks
env_sampler = SetTaskSampler(
lambda: GarageEnv(normalize(HalfCheetahVelEnv())))
env = env_sampler.sample(num_train_tasks)
test_env_sampler = SetTaskSampler(
lambda: GarageEnv(normalize(HalfCheetahVelEnv())))
runner = LocalRunner(ctxt)
# instantiate networks
augmented_env = PEARL.augment_env_spec(env[0](), latent_size)
qf = ContinuousMLPQFunction(env_spec=augmented_env,
hidden_sizes=[net_size, net_size, net_size])
vf_env = PEARL.get_env_spec(env[0](), latent_size, 'vf')
vf = ContinuousMLPQFunction(env_spec=vf_env,
hidden_sizes=[net_size, net_size, net_size])
inner_policy = TanhGaussianMLPPolicy(
env_spec=augmented_env, hidden_sizes=[net_size, net_size, net_size])
pearl = PEARL(
env=env,
policy_class=ContextConditionedPolicy,
encoder_class=MLPEncoder,
inner_policy=inner_policy,
qf=qf,
vf=vf,
num_train_tasks=num_train_tasks,
num_test_tasks=num_test_tasks,
latent_dim=latent_size,
encoder_hidden_sizes=encoder_hidden_sizes,
test_env_sampler=test_env_sampler,
meta_batch_size=meta_batch_size,
num_steps_per_epoch=num_steps_per_epoch,
num_initial_steps=num_initial_steps,
num_tasks_sample=num_tasks_sample,
num_steps_prior=num_steps_prior,
num_extra_rl_steps_posterior=num_extra_rl_steps_posterior,
batch_size=batch_size,
embedding_batch_size=embedding_batch_size,
embedding_mini_batch_size=embedding_mini_batch_size,
max_path_length=max_path_length,
reward_scale=reward_scale,
)
tu.set_gpu_mode(use_gpu, gpu_id=0)
if use_gpu:
pearl.to()
runner.setup(algo=pearl,
env=env[0](),
sampler_cls=LocalSampler,
sampler_args=dict(max_path_length=max_path_length),
n_workers=1,
worker_class=PEARLWorker)
average_returns = runner.train(n_epochs=num_epochs, batch_size=batch_size)
runner.save(num_epochs - 1)
return average_returns
def test_pearl_ml1_push(self):
"""Test PEARL with ML1 Push environment."""
params = dict(seed=1,
num_epochs=1,
num_train_tasks=5,
num_test_tasks=1,
latent_size=7,
encoder_hidden_sizes=[10, 10, 10],
net_size=30,
meta_batch_size=16,
num_steps_per_epoch=40,
num_initial_steps=40,
num_tasks_sample=15,
num_steps_prior=15,
num_extra_rl_steps_posterior=15,
batch_size=256,
embedding_batch_size=8,
embedding_mini_batch_size=8,
max_path_length=50,
reward_scale=10.,
use_information_bottleneck=True,
use_next_obs_in_context=False,
use_gpu=False)
net_size = params['net_size']
set_seed(params['seed'])
env_sampler = SetTaskSampler(
lambda: GarageEnv(normalize(ML1.get_train_tasks('push-v1'))))
env = env_sampler.sample(params['num_train_tasks'])
test_env_sampler = SetTaskSampler(
lambda: GarageEnv(normalize(ML1.get_test_tasks('push-v1'))))
augmented_env = PEARL.augment_env_spec(env[0](), params['latent_size'])
qf = ContinuousMLPQFunction(
env_spec=augmented_env,
hidden_sizes=[net_size, net_size, net_size])
vf_env = PEARL.get_env_spec(env[0](), params['latent_size'], 'vf')
vf = ContinuousMLPQFunction(
env_spec=vf_env, hidden_sizes=[net_size, net_size, net_size])
inner_policy = TanhGaussianMLPPolicy(
env_spec=augmented_env,
hidden_sizes=[net_size, net_size, net_size])
pearl = PEARL(
env=env,
policy_class=ContextConditionedPolicy,
encoder_class=MLPEncoder,
inner_policy=inner_policy,
qf=qf,
vf=vf,
num_train_tasks=params['num_train_tasks'],
num_test_tasks=params['num_test_tasks'],
latent_dim=params['latent_size'],
encoder_hidden_sizes=params['encoder_hidden_sizes'],
meta_batch_size=params['meta_batch_size'],
num_steps_per_epoch=params['num_steps_per_epoch'],
num_initial_steps=params['num_initial_steps'],
num_tasks_sample=params['num_tasks_sample'],
num_steps_prior=params['num_steps_prior'],
num_extra_rl_steps_posterior=params[
'num_extra_rl_steps_posterior'],
batch_size=params['batch_size'],
embedding_batch_size=params['embedding_batch_size'],
embedding_mini_batch_size=params['embedding_mini_batch_size'],
max_path_length=params['max_path_length'],
reward_scale=params['reward_scale'],
)
tu.set_gpu_mode(params['use_gpu'], gpu_id=0)
if params['use_gpu']:
pearl.to()
runner = LocalRunner(snapshot_config)
runner.setup(
algo=pearl,
env=env[0](),
sampler_cls=LocalSampler,
sampler_args=dict(max_path_length=params['max_path_length']),
n_workers=1,
worker_class=PEARLWorker)
worker_args = dict(deterministic=True, accum_context=True)
meta_evaluator = MetaEvaluator(
test_task_sampler=test_env_sampler,
max_path_length=params['max_path_length'],
worker_class=PEARLWorker,
worker_args=worker_args,
n_test_tasks=params['num_test_tasks'])
pearl.evaluator = meta_evaluator
runner.train(n_epochs=params['num_epochs'],
batch_size=params['batch_size'])
def pearl_half_cheetah_vel(ctxt=None,
seed=1,
num_epochs=500,
num_train_tasks=100,
num_test_tasks=30,
latent_size=5,
encoder_hidden_size=200,
net_size=300,
meta_batch_size=16,
num_steps_per_epoch=2000,
num_initial_steps=2000,
num_tasks_sample=5,
num_steps_prior=400,
num_extra_rl_steps_posterior=600,
batch_size=256,
embedding_batch_size=100,
embedding_mini_batch_size=100,
max_path_length=200,
reward_scale=5.,
use_gpu=False):
"""Train PEARL with HalfCheetahVel environment.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by LocalRunner to create the snapshotter.
seed (int): Used to seed the random number generator to produce
determinism.
num_epochs (int): Number of training epochs.
num_train_tasks (int): Number of tasks for training.
num_test_tasks (int): Number of tasks for testing.
latent_size (int): Size of latent context vector.
encoder_hidden_size (int): Output dimension of dense layer of the
context encoder.
net_size (int): Output dimension of a dense layer of Q-function and
value function.
meta_batch_size (int): Meta batch size.
num_steps_per_epoch (int): Number of iterations per epoch.
num_initial_steps (int): Number of transitions obtained per task before
training.
num_tasks_sample (int): Number of random tasks to obtain data for each
iteration.
num_steps_prior (int): Number of transitions to obtain per task with
z ~ prior.
num_extra_rl_steps_posterior (int): Number of additional transitions
to obtain per task with z ~ posterior that are only used to train
the policy and NOT the encoder.
batch_size (int): Number of transitions in RL batch.
embedding_batch_size (int): Number of transitions in context batch.
embedding_mini_batch_size (int): Number of transitions in mini context
batch; should be same as embedding_batch_size for non-recurrent
encoder.
max_path_length (int): Maximum path length.
reward_scale (int): Reward scale.
use_gpu (bool): Whether or not to use GPU for training.
"""
set_seed(seed)
encoder_hidden_sizes = (encoder_hidden_size, encoder_hidden_size,
encoder_hidden_size)
# create multi-task environment and sample tasks
env_sampler = SetTaskSampler(
lambda: GarageEnv(normalize(HalfCheetahVelEnv())))
env = env_sampler.sample(num_train_tasks)
test_env_sampler = SetTaskSampler(
lambda: GarageEnv(normalize(HalfCheetahVelEnv())))
runner = LocalRunner(ctxt)
# instantiate networks
augmented_env = PEARL.augment_env_spec(env[0](), latent_size)
qf = ContinuousMLPQFunction(env_spec=augmented_env,
hidden_sizes=[net_size, net_size, net_size])
vf_env = PEARL.get_env_spec(env[0](), latent_size, 'vf')
vf = ContinuousMLPQFunction(env_spec=vf_env,
hidden_sizes=[net_size, net_size, net_size])
inner_policy = TanhGaussianMLPPolicy(
env_spec=augmented_env, hidden_sizes=[net_size, net_size, net_size])
pearl = PEARL(
env=env,
policy_class=ContextConditionedPolicy,
encoder_class=MLPEncoder,
inner_policy=inner_policy,
qf=qf,
vf=vf,
num_train_tasks=num_train_tasks,
num_test_tasks=num_test_tasks,
latent_dim=latent_size,
encoder_hidden_sizes=encoder_hidden_sizes,
test_env_sampler=test_env_sampler,
meta_batch_size=meta_batch_size,
num_steps_per_epoch=num_steps_per_epoch,
num_initial_steps=num_initial_steps,
num_tasks_sample=num_tasks_sample,
num_steps_prior=num_steps_prior,
num_extra_rl_steps_posterior=num_extra_rl_steps_posterior,
batch_size=batch_size,
embedding_batch_size=embedding_batch_size,
embedding_mini_batch_size=embedding_mini_batch_size,
max_path_length=max_path_length,
reward_scale=reward_scale,
)
set_gpu_mode(use_gpu, gpu_id=0)
if use_gpu:
pearl.to()
runner.setup(algo=pearl,
env=env[0](),
sampler_cls=LocalSampler,
sampler_args=dict(max_path_length=max_path_length),
n_workers=1,
worker_class=PEARLWorker)
runner.train(n_epochs=num_epochs, batch_size=batch_size)
