garage.tf.algos._rl2npo
¶
Natural Policy Gradient Optimization.
- class RL2NPO(env_spec, policy, baseline, sampler, scope=None, discount=0.99, gae_lambda=1, center_adv=True, positive_adv=False, fixed_horizon=False, pg_loss='surrogate', 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', name='NPO')¶
Bases:
garage.tf.algos.NPO
Natural Policy Gradient Optimization.
This is specific for RL^2 (https://arxiv.org/pdf/1611.02779.pdf).
- Parameters
env_spec (EnvSpec) – Environment specification.
policy (garage.tf.policies.StochasticPolicy) – Policy.
baseline (garage.tf.baselines.Baseline) – The baseline.
sampler (garage.sampler.Sampler) – Sampler.
scope (str) – Scope for identifying the algorithm. Must be specified if running multiple algorithms simultaneously, each using different environments and policies.
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.
pg_loss (str) – A string from: ‘vanilla’, ‘surrogate’, ‘surrogate_clip’. The type of loss functions to use.
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.
fit_baseline (str) – Either ‘before’ or ‘after’. See above docstring for a more detail explanation. Currently it only supports ‘before’.
name (str) – The name of the algorithm.
- optimize_policy(episodes)¶
Optimize policy.
- Parameters
episodes (EpisodeBatch) – Batch of episodes.