def torch_pearl_ml1_push(ctxt=None,
seed=1,
num_epochs=1000,
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=150,
reward_scale=10.,
use_gpu=False):
"""Train 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)
# create multi-task environment and sample tasks
env_sampler = SetTaskSampler(
lambda: GarageEnv(normalize(ML1.get_train_tasks('push-v1'))))
env = env_sampler.sample(num_train_tasks)
test_env_sampler = SetTaskSampler(
lambda: GarageEnv(normalize(ML1.get_test_tasks('push-v1'))))
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,
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)
worker_args = dict(deterministic=True, accum_context=True)
meta_evaluator = MetaEvaluator(test_task_sampler=test_env_sampler,
max_path_length=max_path_length,
worker_class=PEARLWorker,
worker_args=worker_args,
n_test_tasks=num_test_tasks)
pearl.evaluator = meta_evaluator
runner.train(n_epochs=num_epochs, batch_size=batch_size)
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'])