garage.torch.algos.ppo module¶
Proximal Policy Optimization (PPO).
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class
PPO
(env_spec, policy, value_function, policy_optimizer=None, vf_optimizer=None, max_path_length=500, lr_clip_range=0.2, num_train_per_epoch=1, discount=0.99, gae_lambda=0.97, center_adv=True, positive_adv=False, policy_ent_coeff=0.0, use_softplus_entropy=False, stop_entropy_gradient=False, entropy_method='no_entropy')[source]¶ Bases:
garage.torch.algos.vpg.VPG
Proximal Policy Optimization (PPO).
Parameters: - env_spec (garage.envs.EnvSpec) – Environment specification.
- policy (garage.torch.policies.Policy) – Policy.
- value_function (garage.torch.value_functions.ValueFunction) – The value function.
- policy_optimizer (garage.torch.optimizer.OptimizerWrapper) – Optimizer for policy.
- vf_optimizer (garage.torch.optimizer.OptimizerWrapper) – Optimizer for value function.
- max_path_length (int) – Maximum length of a single rollout.
- lr_clip_range (float) – The limit on the likelihood ratio between policies.
- num_train_per_epoch (int) – Number of train_once calls per epoch.
- 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.
- 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.
- 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.