def run_garage_pytorch(env, seed, log_dir):
"""Create garage PyTorch VPG 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
"""
env = TfEnv(normalize(env))
deterministic.set_seed(seed)
runner = LocalRunner(snapshot_config)
policy = PyTorch_GMP(env.spec,
hidden_sizes=hyper_parameters['hidden_sizes'],
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None)
value_function = GaussianMLPValueFunction(env_spec=env.spec,
hidden_sizes=(32, 32),
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None)
policy_optimizer = OptimizerWrapper((torch.optim.Adam, dict(lr=2.5e-4)),
policy,
max_optimization_epochs=10,
minibatch_size=64)
vf_optimizer = OptimizerWrapper((torch.optim.Adam, dict(lr=2.5e-4)),
value_function,
max_optimization_epochs=10,
minibatch_size=64)
algo = PyTorch_VPG(env_spec=env.spec,
policy=policy,
value_function=value_function,
policy_optimizer=policy_optimizer,
vf_optimizer=vf_optimizer,
max_path_length=hyper_parameters['max_path_length'],
discount=hyper_parameters['discount'],
center_adv=hyper_parameters['center_adv'])
# Set up logger since we are not using run_experiment
tabular_log_file = osp.join(log_dir, 'progress.csv')
dowel_logger.add_output(dowel.StdOutput())
dowel_logger.add_output(dowel.CsvOutput(tabular_log_file))
dowel_logger.add_output(dowel.TensorBoardOutput(log_dir))
runner.setup(algo, env)
runner.train(n_epochs=hyper_parameters['n_epochs'],
batch_size=hyper_parameters['batch_size'])
dowel_logger.remove_all()
return tabular_log_file
def ppo_garage_pytorch(ctxt, env_id, seed):
"""Create garage PyTorch PPO model and training.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
"""
deterministic.set_seed(seed)
trainer = Trainer(ctxt)
env = normalize(GymEnv(env_id))
policy = PyTorch_GMP(env.spec,
hidden_sizes=(32, 32),
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None)
value_function = GaussianMLPValueFunction(env_spec=env.spec,
hidden_sizes=(32, 32),
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None)
policy_optimizer = OptimizerWrapper((torch.optim.Adam, dict(lr=2.5e-4)),
policy,
max_optimization_epochs=10,
minibatch_size=64)
vf_optimizer = OptimizerWrapper((torch.optim.Adam, dict(lr=2.5e-4)),
value_function,
max_optimization_epochs=10,
minibatch_size=64)
sampler = RaySampler(agents=policy,
envs=env,
max_episode_length=env.spec.max_episode_length)
algo = PyTorch_PPO(env_spec=env.spec,
policy=policy,
value_function=value_function,
sampler=sampler,
policy_optimizer=policy_optimizer,
vf_optimizer=vf_optimizer,
discount=0.99,
gae_lambda=0.95,
center_adv=True,
lr_clip_range=0.2)
trainer.setup(algo, env)
trainer.train(n_epochs=hyper_parameters['n_epochs'],
batch_size=hyper_parameters['batch_size'])
def run_garage_pytorch(env, seed, log_dir):
"""Create garage PyTorch 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
"""
env = TfEnv(normalize(env))
deterministic.set_seed(seed)
runner = LocalRunner(snapshot_config)
policy = PyTorch_GMP(env.spec,
hidden_sizes=(32, 32),
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None)
value_functions = LinearFeatureBaseline(env_spec=env.spec)
algo = PyTorch_PPO(env_spec=env.spec,
policy=policy,
value_function=value_functions,
optimizer=torch.optim.Adam,
policy_lr=3e-4,
max_path_length=hyper_parameters['max_path_length'],
discount=0.99,
gae_lambda=0.95,
center_adv=True,
lr_clip_range=0.2,
minibatch_size=128,
max_optimization_epochs=10)
# Set up logger since we are not using run_experiment
tabular_log_file = osp.join(log_dir, 'progress.csv')
dowel_logger.add_output(dowel.StdOutput())
dowel_logger.add_output(dowel.CsvOutput(tabular_log_file))
dowel_logger.add_output(dowel.TensorBoardOutput(log_dir))
runner.setup(algo, env)
runner.train(n_epochs=hyper_parameters['n_epochs'],
batch_size=hyper_parameters['batch_size'])
dowel_logger.remove_all()
return tabular_log_file
def vpg_garage_pytorch(ctxt, env_id, seed):
"""Create garage PyTorch VPG model and training.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by LocalRunner to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
"""
deterministic.set_seed(seed)
runner = LocalRunner(ctxt)
env = GarageEnv(normalize(gym.make(env_id)))
policy = PyTorch_GMP(env.spec,
hidden_sizes=hyper_parameters['hidden_sizes'],
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None)
value_function = GaussianMLPValueFunction(env_spec=env.spec,
hidden_sizes=(32, 32),
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None)
policy_optimizer = OptimizerWrapper((torch.optim.Adam, dict(lr=2.5e-4)),
policy,
max_optimization_epochs=10,
minibatch_size=64)
vf_optimizer = OptimizerWrapper((torch.optim.Adam, dict(lr=2.5e-4)),
value_function,
max_optimization_epochs=10,
minibatch_size=64)
algo = PyTorch_VPG(
env_spec=env.spec,
policy=policy,
value_function=value_function,
policy_optimizer=policy_optimizer,
vf_optimizer=vf_optimizer,
max_episode_length=hyper_parameters['max_episode_length'],
discount=hyper_parameters['discount'],
center_adv=hyper_parameters['center_adv'])
runner.setup(algo, env)
runner.train(n_epochs=hyper_parameters['n_epochs'],
batch_size=hyper_parameters['batch_size'])
def run_garage_pytorch(env, seed, log_dir):
"""Create garage PyTorch 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
"""
env = TfEnv(normalize(env))
deterministic.set_seed(seed)
runner = LocalRunner(snapshot_config)
policy = PyTorch_GMP(env.spec,
hidden_sizes=hyper_parameters['hidden_sizes'],
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = PyTorch_TRPO(
env_spec=env.spec,
policy=policy,
baseline=baseline,
max_path_length=hyper_parameters['max_path_length'],
discount=hyper_parameters['discount'],
gae_lambda=hyper_parameters['gae_lambda'],
max_kl=hyper_parameters['max_kl'],
)
# Set up logger since we are not using run_experiment
tabular_log_file = osp.join(log_dir, 'progress.csv')
dowel_logger.add_output(dowel.StdOutput())
dowel_logger.add_output(dowel.CsvOutput(tabular_log_file))
dowel_logger.add_output(dowel.TensorBoardOutput(log_dir))
runner.setup(algo, env)
runner.train(n_epochs=hyper_parameters['n_epochs'],
batch_size=hyper_parameters['batch_size'])
dowel_logger.remove_all()
return tabular_log_file
def trpo_garage_pytorch(ctxt, env_id, seed):
"""Create garage PyTorch TRPO model and training.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
"""
deterministic.set_seed(seed)
trainer = Trainer(ctxt)
env = normalize(GymEnv(env_id))
policy = PyTorch_GMP(env.spec,
hidden_sizes=hyper_parameters['hidden_sizes'],
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None)
value_function = GaussianMLPValueFunction(env_spec=env.spec,
hidden_sizes=(32, 32),
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None)
sampler = LocalSampler(agents=policy,
envs=env,
max_episode_length=env.spec.max_episode_length)
algo = PyTorch_TRPO(env_spec=env.spec,
policy=policy,
value_function=value_function,
sampler=sampler,
discount=hyper_parameters['discount'],
gae_lambda=hyper_parameters['gae_lambda'])
trainer.setup(algo, env)
trainer.train(n_epochs=hyper_parameters['n_epochs'],
batch_size=hyper_parameters['batch_size'])
def ppo_garage_pytorch(ctxt, env_id, seed):
"""Create garage PyTorch PPO model and training.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by LocalRunner to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
"""
deterministic.set_seed(seed)
runner = LocalRunner(ctxt)
env = TfEnv(normalize(gym.make(env_id)))
policy = PyTorch_GMP(env.spec,
hidden_sizes=(32, 32),
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None)
value_functions = LinearFeatureBaseline(env_spec=env.spec)
algo = PyTorch_PPO(env_spec=env.spec,
policy=policy,
value_function=value_functions,
optimizer=torch.optim.Adam,
policy_lr=3e-4,
max_path_length=hyper_parameters['max_path_length'],
discount=0.99,
gae_lambda=0.95,
center_adv=True,
lr_clip_range=0.2,
minibatch_size=128,
max_optimization_epochs=10)
runner.setup(algo, env)
runner.train(n_epochs=hyper_parameters['n_epochs'],
batch_size=hyper_parameters['batch_size'])
def run_garage_pytorch(env, seed, log_dir):
"""Create garage PyTorch VPG model and training."""
env = TfEnv(normalize(env))
deterministic.set_seed(seed)
runner = LocalRunner(snapshot_config)
policy = PyTorch_GMP(
env.spec,
hidden_sizes=hyper_parameters['hidden_sizes'],
hidden_nonlinearity=torch.tanh,
# init_std=1e-6,
output_nonlinearity=None)
baseline = LinearFeatureBaseline(env_spec=env.spec)
algo = PyTorch_VPG(env_spec=env.spec,
policy=policy,
optimizer=torch.optim.Adam,
baseline=baseline,
max_path_length=hyper_parameters['max_path_length'],
discount=hyper_parameters['discount'],
center_adv=hyper_parameters['center_adv'],
policy_lr=hyper_parameters['learning_rate'])
# Set up logger since we are not using run_experiment
tabular_log_file = osp.join(log_dir, 'progress.csv')
dowel_logger.add_output(dowel.StdOutput())
dowel_logger.add_output(dowel.CsvOutput(tabular_log_file))
dowel_logger.add_output(dowel.TensorBoardOutput(log_dir))
runner.setup(algo, env)
runner.train(n_epochs=hyper_parameters['n_epochs'], batch_size=2048)
dowel_logger.remove_all()
return tabular_log_file
def vpg_garage_pytorch(ctxt, env_id, seed):
"""Create garage PyTorch VPG model and training.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by LocalRunner to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
"""
deterministic.set_seed(seed)
runner = LocalRunner(ctxt)
env = TfEnv(normalize(gym.make(env_id)))
policy = PyTorch_GMP(env.spec,
hidden_sizes=hyper_parameters['hidden_sizes'],
hidden_nonlinearity=torch.tanh,
output_nonlinearity=None)
value_function = LinearFeatureBaseline(env_spec=env.spec)
algo = PyTorch_VPG(env_spec=env.spec,
policy=policy,
optimizer=torch.optim.Adam,
policy_lr=hyper_parameters['learning_rate'],
value_function=value_function,
max_path_length=hyper_parameters['max_path_length'],
discount=hyper_parameters['discount'],
center_adv=hyper_parameters['center_adv'])
runner.setup(algo, env)
runner.train(n_epochs=hyper_parameters['n_epochs'],
batch_size=hyper_parameters['batch_size'])