garage.torch.algos.ddpg module¶
This modules creates a DDPG model in PyTorch.
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class
DDPG(env_spec, policy, qf, replay_buffer, *, steps_per_epoch=20, n_train_steps=50, max_path_length=None, max_eval_path_length=None, buffer_batch_size=64, min_buffer_size=10000, rollout_batch_size=1, exploration_policy=None, target_update_tau=0.01, discount=0.99, policy_weight_decay=0, qf_weight_decay=0, policy_optimizer=<sphinx.ext.autodoc.importer._MockObject object>, qf_optimizer=<sphinx.ext.autodoc.importer._MockObject object>, policy_lr=<garage._functions._Default object>, qf_lr=<garage._functions._Default object>, clip_pos_returns=False, clip_return=inf, max_action=None, reward_scale=1.0, smooth_return=True)[source]¶ Bases:
garage.np.algos.rl_algorithm.RLAlgorithmA DDPG model implemented with PyTorch.
DDPG, also known as Deep Deterministic Policy Gradient, uses actor-critic method to optimize the policy and Q-function prediction. It uses a supervised method to update the critic network and policy gradient to update the actor network. And there are exploration strategy, replay buffer and target networks involved to stabilize the training process.
Parameters: - env_spec (EnvSpec) – Environment specification.
- policy (garage.torch.policies.Policy) – Policy.
- qf (object) – Q-value network.
- replay_buffer (garage.replay_buffer.ReplayBuffer) – Replay buffer.
- steps_per_epoch (int) – Number of train_once calls per epoch.
- n_train_steps (int) – Training steps.
- max_path_length (int) – Maximum path length. The episode will terminate when length of trajectory reaches max_path_length.
- max_eval_path_length (int or None) – Maximum length of paths used for off-policy evaluation. If None, defaults to max_path_length.
- buffer_batch_size (int) – Batch size of replay buffer.
- min_buffer_size (int) – The minimum buffer size for replay buffer.
- rollout_batch_size (int) – Roll out batch size.
- exploration_policy (garage.np.exploration_policies.ExplorationPolicy) – # noqa: E501 Exploration strategy.
- target_update_tau (float) – Interpolation parameter for doing the soft target update.
- discount (float) – Discount factor for the cumulative return.
- policy_weight_decay (float) – L2 weight decay factor for parameters of the policy network.
- qf_weight_decay (float) – L2 weight decay factor for parameters of the q value network.
- policy_optimizer (Union[type, tuple[type, dict]]) – Type of optimizer for training policy network. This can be an optimizer type such as torch.optim.Adam or a tuple of type and dictionary, where dictionary contains arguments to initialize the optimizer e.g. (torch.optim.Adam, {‘lr’ : 1e-3}).
- qf_optimizer (Union[type, tuple[type, dict]]) – Type of optimizer for training Q-value network. This can be an optimizer type such as torch.optim.Adam or a tuple of type and dictionary, where dictionary contains arguments to initialize the optimizer e.g. (torch.optim.Adam, {‘lr’ : 1e-3}).
- policy_lr (float) – Learning rate for policy network parameters.
- qf_lr (float) – Learning rate for Q-value network parameters.
- clip_pos_returns (bool) – Whether or not clip positive returns.
- clip_return (float) – Clip return to be in [-clip_return, clip_return].
- max_action (float) – Maximum action magnitude.
- reward_scale (float) – Reward scale.
- smooth_return (bool) – Whether to smooth the return for logging.
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optimize_policy(samples_data)[source]¶ Perform algorithm optimizing.
Parameters: samples_data (dict) – Processed batch data. Returns: Loss of action predicted by the policy network. qval_loss: Loss of Q-value predicted by the Q-network. ys: y_s. qval: Q-value predicted by the Q-network. Return type: action_loss
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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