def run_metarl(env, envs, tasks, seed, log_dir):
"""Create metarl Tensorflow PPO model and training.
Args:
env (dict): Environment of the task.
seed (int): Random positive integer for the trial.
log_dir (str): Log dir path.
Returns:
str: Path to output csv file
"""
deterministic.set_seed(seed)
snapshot_config = SnapshotConfig(snapshot_dir=log_dir,
snapshot_mode='gap',
snapshot_gap=10)
with LocalTFRunner(snapshot_config) as runner:
policy = GaussianGRUPolicy(
hidden_dims=hyper_parameters['hidden_sizes'],
env_spec=env.spec,
state_include_action=False)
baseline = MetaRLLinearFeatureBaseline(env_spec=env.spec)
inner_algo = RL2PPO(
env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=hyper_parameters['max_path_length'] * hyper_parameters['rollout_per_task'],
discount=hyper_parameters['discount'],
gae_lambda=hyper_parameters['gae_lambda'],
lr_clip_range=hyper_parameters['lr_clip_range'],
optimizer_args=dict(
max_epochs=hyper_parameters['optimizer_max_epochs'],
tf_optimizer_args=dict(
learning_rate=hyper_parameters['optimizer_lr'],
),
)
)
# Need to pass this if meta_batch_size < num_of_tasks
task_names = list(ML45_ENVS['train'].keys())
algo = RL2(
policy=policy,
inner_algo=inner_algo,
max_path_length=hyper_parameters['max_path_length'],
meta_batch_size=hyper_parameters['meta_batch_size'],
task_sampler=tasks,
task_names=None if hyper_parameters['meta_batch_size'] >= len(task_names) else task_names)
# Set up logger since we are not using run_experiment
tabular_log_file = osp.join(log_dir, 'progress.csv')
text_log_file = osp.join(log_dir, 'debug.log')
dowel_logger.add_output(dowel.TextOutput(text_log_file))
dowel_logger.add_output(dowel.CsvOutput(tabular_log_file))
dowel_logger.add_output(dowel.StdOutput())
dowel_logger.add_output(dowel.TensorBoardOutput(log_dir))
runner.setup(
algo,
envs,
sampler_cls=hyper_parameters['sampler_cls'],
n_workers=hyper_parameters['meta_batch_size'],
worker_class=RL2Worker,
sampler_args=dict(
use_all_workers=hyper_parameters['use_all_workers']),
worker_args=dict(
n_paths_per_trial=hyper_parameters['rollout_per_task']))
# meta evaluator
env_obs_dim = [env().observation_space.shape[0] for (_, env) in ML45_ENVS['test'].items()]
max_obs_dim = max(env_obs_dim)
ML_test_envs = [
TaskIdWrapper(NormalizedRewardEnv(RL2Env(env(*ML45_ARGS['test'][task]['args'],
**ML45_ARGS['test'][task]['kwargs']), max_obs_dim)), task_id=task_id, task_name=task)
for (task_id, (task, env)) in enumerate(ML45_ENVS['test'].items())
]
test_tasks = task_sampler.EnvPoolSampler(ML_test_envs)
test_tasks.grow_pool(hyper_parameters['n_test_tasks'])
test_task_names = list(ML45_ENVS['test'].keys())
runner.setup_meta_evaluator(test_task_sampler=test_tasks,
n_exploration_traj=hyper_parameters['rollout_per_task'],
n_test_rollouts=hyper_parameters['test_rollout_per_task'],
n_test_tasks=hyper_parameters['n_test_tasks'],
n_workers=hyper_parameters['n_test_tasks'],
test_task_names=None if hyper_parameters['n_test_tasks'] >= len(test_task_names) else test_task_names)
runner.train(n_epochs=hyper_parameters['n_itr'],
batch_size=hyper_parameters['meta_batch_size'] * hyper_parameters['rollout_per_task'] * hyper_parameters['max_path_length'])
dowel_logger.remove_all()
return tabular_log_file
def run_metarl(env, seed, log_dir):
"""Create metarl Tensorflow PPO model and training.
Args:
env (dict): Environment of the task.
seed (int): Random positive integer for the trial.
log_dir (str): Log dir path.
Returns:
str: Path to output csv file
"""
deterministic.set_seed(seed)
snapshot_config = SnapshotConfig(snapshot_dir=log_dir,
snapshot_mode='gap',
snapshot_gap=10)
with LocalTFRunner(snapshot_config) as runner:
env, task_samplers = _prepare_meta_env(env)
policy = GaussianGRUPolicy(
hidden_dims=hyper_parameters['hidden_sizes'],
env_spec=env.spec,
state_include_action=False)
baseline = MetaRLLinearFeatureBaseline(env_spec=env.spec)
inner_algo = RL2PPO(
env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=hyper_parameters['max_path_length'] *
hyper_parameters['rollout_per_task'],
discount=hyper_parameters['discount'],
gae_lambda=hyper_parameters['gae_lambda'],
lr_clip_range=hyper_parameters['lr_clip_range'],
optimizer_args=dict(
max_epochs=hyper_parameters['optimizer_max_epochs'],
tf_optimizer_args=dict(
learning_rate=hyper_parameters['optimizer_lr'], ),
))
algo = RL2(policy=policy,
inner_algo=inner_algo,
max_path_length=hyper_parameters['max_path_length'],
meta_batch_size=hyper_parameters['meta_batch_size'],
task_sampler=task_samplers)
# Set up logger since we are not using run_experiment
tabular_log_file = osp.join(log_dir, 'progress.csv')
text_log_file = osp.join(log_dir, 'debug.log')
dowel_logger.add_output(dowel.TextOutput(text_log_file))
dowel_logger.add_output(dowel.CsvOutput(tabular_log_file))
dowel_logger.add_output(dowel.StdOutput())
dowel_logger.add_output(dowel.TensorBoardOutput(log_dir))
runner.setup(
algo,
task_samplers.sample(hyper_parameters['meta_batch_size']),
sampler_cls=hyper_parameters['sampler_cls'],
n_workers=hyper_parameters['meta_batch_size'],
worker_class=RL2Worker,
sampler_args=dict(
use_all_workers=hyper_parameters['use_all_workers']),
worker_args=dict(
n_paths_per_trial=hyper_parameters['rollout_per_task']))
runner.setup_meta_evaluator(
test_task_sampler=task_samplers,
n_exploration_traj=hyper_parameters['rollout_per_task'],
n_test_rollouts=hyper_parameters['test_rollout_per_task'],
n_test_tasks=hyper_parameters['n_test_tasks'],
n_workers=hyper_parameters['n_test_tasks'])
runner.train(n_epochs=hyper_parameters['n_itr'],
batch_size=hyper_parameters['meta_batch_size'] *
hyper_parameters['rollout_per_task'] *
hyper_parameters['max_path_length'])
dowel_logger.remove_all()
return tabular_log_file