def test_all_ml45():
ml45_train_env = ML45.get_train_tasks()
train_tasks = ml45_train_env.sample_tasks(46)
for t in train_tasks:
ml45_train_env.set_task(t)
step_env(ml45_train_env, max_path_length=3)
ml45_train_env.close()
del ml45_train_env
ml45_test_env = ML45.get_test_tasks()
test_tasks = ml45_test_env.sample_tasks(6)
for t in test_tasks:
ml45_test_env.set_task(t)
step_env(ml45_test_env, max_path_length=3)
ml45_test_env.close()
del ml45_test_env
def test_static_task_ids(task_name):
env = ML45.from_task(task_name)
assert env.active_task == list(ALL_V1_ENVIRONMENTS.keys()).index(task_name)
def run_task(snapshot_config, *_):
"""Set up environment and algorithm and run the task.
Args:
snapshot_config (metarl.experiment.SnapshotConfig): The snapshot
configuration used by LocalRunner to create the snapshotter.
If None, it will create one with default settings.
_ : Unused parameters
"""
# create multi-task environment and sample tasks
ML_train_envs = [
TaskIdWrapper(MetaRLEnv(
normalize(
env(*ML45_ARGS['train'][task]['args'],
**ML45_ARGS['train'][task]['kwargs']))),
task_id=task_id,
task_name=task,
pad=True)
for (task_id, (task, env)) in enumerate(ML45_ENVS['train'].items())
]
ML_test_envs = [
TaskIdWrapper(MetaRLEnv(
normalize(
env(*ML45_ARGS['test'][task]['args'],
**ML45_ARGS['test'][task]['kwargs']))),
task_id=task_id,
task_name=task,
pad=True)
for (task_id, (task, env)) in enumerate(ML45_ENVS['test'].items())
]
train_task_names = ML45.get_train_tasks()._task_names
test_task_names = ML45.get_test_tasks()._task_names
env_sampler = EnvPoolSampler(ML_train_envs)
env = env_sampler.sample(params['num_train_tasks'])
test_env_sampler = EnvPoolSampler(ML_test_envs)
test_env = test_env_sampler.sample(params['num_test_tasks'])
runner = LocalRunner(snapshot_config)
obs_dim = max(
int(np.prod(env[i]().observation_space.shape))
for i in range(params['num_train_tasks']))
action_dim = int(np.prod(env[0]().action_space.shape))
reward_dim = 1
# instantiate networks
encoder_in_dim = obs_dim + action_dim + reward_dim
encoder_out_dim = params['latent_size'] * 2
net_size = params['net_size']
context_encoder = MLPEncoder(input_dim=encoder_in_dim,
output_dim=encoder_out_dim,
hidden_sizes=[200, 200, 200])
space_a = akro.Box(low=-1,
high=1,
shape=(obs_dim + params['latent_size'], ),
dtype=np.float32)
space_b = akro.Box(low=-1, high=1, shape=(action_dim, ), dtype=np.float32)
augmented_env = EnvSpec(space_a, space_b)
qf1 = ContinuousMLPQFunction(env_spec=augmented_env,
hidden_sizes=[net_size, net_size, net_size])
qf2 = ContinuousMLPQFunction(env_spec=augmented_env,
hidden_sizes=[net_size, net_size, net_size])
obs_space = akro.Box(low=-1, high=1, shape=(obs_dim, ), dtype=np.float32)
action_space = akro.Box(low=-1,
high=1,
shape=(params['latent_size'], ),
dtype=np.float32)
vf_env = EnvSpec(obs_space, action_space)
vf = ContinuousMLPQFunction(env_spec=vf_env,
hidden_sizes=[net_size, net_size, net_size])
policy = TanhGaussianMLPPolicy2(
env_spec=augmented_env, hidden_sizes=[net_size, net_size, net_size])
context_conditioned_policy = ContextConditionedPolicy(
latent_dim=params['latent_size'],
context_encoder=context_encoder,
policy=policy,
use_ib=params['use_information_bottleneck'],
use_next_obs=params['use_next_obs_in_context'],
)
pearlsac = PEARLSAC(
env=env,
test_env=test_env,
policy=context_conditioned_policy,
qf1=qf1,
qf2=qf2,
vf=vf,
num_train_tasks=params['num_train_tasks'],
num_test_tasks=params['num_test_tasks'],
latent_dim=params['latent_size'],
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'],
num_evals=params['num_evals'],
num_steps_per_eval=params['num_steps_per_eval'],
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'],
train_task_names=train_task_names,
test_task_names=test_task_names,
)
tu.set_gpu_mode(params['use_gpu'], gpu_id=0)
if params['use_gpu']:
pearlsac.to()
runner.setup(algo=pearlsac,
env=env,
sampler_cls=PEARLSampler,
sampler_args=dict(max_path_length=params['max_path_length']))
runner.train(n_epochs=params['num_epochs'],
batch_size=params['batch_size'])
def run_metarl(env, test_env, seed, log_dir):
"""Create metarl model and training."""
deterministic.set_seed(seed)
snapshot_config = SnapshotConfig(snapshot_dir=log_dir,
snapshot_mode='gap',
snapshot_gap=10)
runner = LocalRunner(snapshot_config)
obs_dim = max(
int(np.prod(env[i]().observation_space.shape))
for i in range(params['num_train_tasks']))
action_dim = int(np.prod(env[0]().action_space.shape))
reward_dim = 1
# instantiate networks
encoder_in_dim = obs_dim + action_dim + reward_dim
encoder_out_dim = params['latent_size'] * 2
net_size = params['net_size']
context_encoder = MLPEncoder(input_dim=encoder_in_dim,
output_dim=encoder_out_dim,
hidden_sizes=[200, 200, 200])
space_a = akro.Box(low=-1,
high=1,
shape=(obs_dim + params['latent_size'], ),
dtype=np.float32)
space_b = akro.Box(low=-1, high=1, shape=(action_dim, ), dtype=np.float32)
augmented_env = EnvSpec(space_a, space_b)
qf1 = ContinuousMLPQFunction(env_spec=augmented_env,
hidden_sizes=[net_size, net_size, net_size])
qf2 = ContinuousMLPQFunction(env_spec=augmented_env,
hidden_sizes=[net_size, net_size, net_size])
obs_space = akro.Box(low=-1, high=1, shape=(obs_dim, ), dtype=np.float32)
action_space = akro.Box(low=-1,
high=1,
shape=(params['latent_size'], ),
dtype=np.float32)
vf_env = EnvSpec(obs_space, action_space)
vf = ContinuousMLPQFunction(env_spec=vf_env,
hidden_sizes=[net_size, net_size, net_size])
policy = TanhGaussianMLPPolicy2(
env_spec=augmented_env, hidden_sizes=[net_size, net_size, net_size])
context_conditioned_policy = ContextConditionedPolicy(
latent_dim=params['latent_size'],
context_encoder=context_encoder,
policy=policy,
use_ib=params['use_information_bottleneck'],
use_next_obs=params['use_next_obs_in_context'],
)
train_task_names = ML45.get_train_tasks()._task_names
test_task_names = ML45.get_test_tasks()._task_names
pearlsac = PEARLSAC(
env=env,
test_env=test_env,
policy=context_conditioned_policy,
qf1=qf1,
qf2=qf2,
vf=vf,
num_train_tasks=params['num_train_tasks'],
num_test_tasks=params['num_test_tasks'],
latent_dim=params['latent_size'],
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'],
num_evals=params['num_evals'],
num_steps_per_eval=params['num_steps_per_eval'],
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'],
train_task_names=train_task_names,
test_task_names=test_task_names,
)
tu.set_gpu_mode(params['use_gpu'], gpu_id=0)
if params['use_gpu']:
pearlsac.to()
tabular_log_file = osp.join(log_dir, 'progress.csv')
tensorboard_log_dir = osp.join(log_dir)
dowel_logger.add_output(dowel.StdOutput())
dowel_logger.add_output(dowel.CsvOutput(tabular_log_file))
dowel_logger.add_output(dowel.TensorBoardOutput(tensorboard_log_dir))
runner.setup(algo=pearlsac,
env=env,
sampler_cls=PEARLSampler,
sampler_args=dict(max_path_length=params['max_path_length']))
runner.train(n_epochs=params['num_epochs'],
batch_size=params['batch_size'])
dowel_logger.remove_all()
return tabular_log_file
def torch_pearl_ml45(ctxt=None,
seed=1,
num_epochs=1000,
num_train_tasks=45,
num_test_tasks=5,
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 ML45 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
ML_train_envs = [
GarageEnv(normalize(ML45.from_task(task_name)))
for task_name in ML45.get_train_tasks().all_task_names
]
ML_test_envs = [
GarageEnv(normalize(ML45.from_task(task_name)))
for task_name in ML45.get_test_tasks().all_task_names
]
env_sampler = EnvPoolSampler(ML_train_envs)
env_sampler.grow_pool(num_train_tasks)
env = env_sampler.sample(num_train_tasks)
test_env_sampler = EnvPoolSampler(ML_test_envs)
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)
runner.train(n_epochs=num_epochs, batch_size=batch_size)
def test_random_init_train():
"""Test that random_init == True for all envs."""
env = ML45(env_type='train')
assert len(env._task_envs) == 45
for task_env in env._task_envs:
assert task_env.random_init