garage.torch.algos.vpg module¶
Vanilla Policy Gradient (REINFORCE).
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class
VPG(env_spec, policy, value_function, policy_optimizer=None, vf_optimizer=None, max_path_length=500, num_train_per_epoch=1, discount=0.99, gae_lambda=1, 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.np.algos.rl_algorithm.RLAlgorithmVanilla Policy Gradient (REINFORCE).
VPG, also known as Reinforce, trains stochastic policy in an on-policy way.
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.
- 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.
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process_samples(paths)[source]¶ Process sample data based on the collected paths.
Notes: P is the maximum path length (self.max_path_length)
Parameters: paths (list[dict]) – A list of collected paths Returns: - The observations of the environment
- with shape \((N, P, O*)\).
- torch.Tensor: The actions fed to the environment
- with shape \((N, P, A*)\).
torch.Tensor: The acquired rewards with shape \((N, P)\). list[int]: Numbers of valid steps in each paths. torch.Tensor: Value function estimation at each step
with shape \((N, P)\).Return type: torch.Tensor
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train(runner)[source]¶ Obtain samplers and start actual training for each epoch.
Parameters: runner (LocalRunner) – LocalRunner is passed to give algorithm the access to runner.step_epochs(), which provides services such as snapshotting and sampler control. Returns: The average return in last epoch cycle. Return type: float