"""Vanilla Policy Gradient."""
from garage.tf.algos.npo import NPO
from garage.tf.optimizers import FirstOrderOptimizer
[docs]class VPG(NPO):
"""Vanilla Policy Gradient.
Args:
env_spec (garage.envs.EnvSpec): Environment specification.
policy (garage.tf.policies.StochasticPolicy): Policy.
baseline (garage.tf.baselines.Baseline): The baseline.
scope (str): Scope for identifying the algorithm.
Must be specified if running multiple algorithms
simultaneously, each using different environments
and policies.
max_path_length (int): Maximum length of a single rollout.
discount (float): Discount.
gae_lambda (float): Lambda used for generalized advantage
estimation.
center_adv (bool): Whether to rescale the advantages
so that they have mean 0 and standard deviation 1.
positive_adv (bool): Whether to shift the advantages
so that they are always positive. When used in
conjunction with center_adv the advantages will be
standardized before shifting.
fixed_horizon (bool): Whether to fix horizon.
lr_clip_range (float): The limit on the likelihood ratio between
policies, as in PPO.
max_kl_step (float): The maximum KL divergence between old and new
policies, as in TRPO.
optimizer (object): The optimizer of the algorithm. Should be the
optimizers in garage.tf.optimizers.
optimizer_args (dict): The arguments of the optimizer.
policy_ent_coeff (float): The coefficient of the policy entropy.
Setting it to zero would mean no entropy regularization.
use_softplus_entropy (bool): Whether to estimate the softmax
distribution of the entropy to prevent the entropy from being
negative.
use_neg_logli_entropy (bool): Whether to estimate the entropy as the
negative log likelihood of the action.
stop_entropy_gradient (bool): Whether to stop the entropy gradient.
entropy_method (str): A string from: 'max', 'regularized',
'no_entropy'. The type of entropy method to use. 'max' adds the
dense entropy to the reward for each time step. 'regularized' adds
the mean entropy to the surrogate objective. See
https://arxiv.org/abs/1805.00909 for more details.
flatten_input (bool): Whether to flatten input along the observation
dimension. If True, for example, an observation with shape (2, 4)
will be flattened to 8.
name (str): The name of the algorithm.
"""
def __init__(self,
env_spec,
policy,
baseline,
scope=None,
max_path_length=500,
discount=0.99,
gae_lambda=1,
center_adv=True,
positive_adv=False,
fixed_horizon=False,
lr_clip_range=0.01,
max_kl_step=0.01,
optimizer=None,
optimizer_args=None,
policy_ent_coeff=0.0,
use_softplus_entropy=False,
use_neg_logli_entropy=False,
stop_entropy_gradient=False,
entropy_method='no_entropy',
flatten_input=True,
name='VPG'):
if optimizer is None:
default_args = dict(
batch_size=None,
max_epochs=1,
)
optimizer = FirstOrderOptimizer
if optimizer_args is None:
optimizer_args = default_args
else:
optimizer_args = dict(default_args, **optimizer_args)
super().__init__(env_spec=env_spec,
policy=policy,
baseline=baseline,
scope=scope,
max_path_length=max_path_length,
discount=discount,
gae_lambda=gae_lambda,
center_adv=center_adv,
positive_adv=positive_adv,
fixed_horizon=fixed_horizon,
pg_loss='vanilla',
lr_clip_range=lr_clip_range,
max_kl_step=max_kl_step,
optimizer=optimizer,
optimizer_args=optimizer_args,
policy_ent_coeff=policy_ent_coeff,
use_softplus_entropy=use_softplus_entropy,
use_neg_logli_entropy=use_neg_logli_entropy,
stop_entropy_gradient=stop_entropy_gradient,
entropy_method=entropy_method,
name=name)