garage.tf.algos.ddpg module¶
Deep Deterministic Policy Gradient (DDPG) implementation in TensorFlow.
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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=<class 'tensorflow.python.training.adam.AdamOptimizer'>, qf_optimizer=<class 'tensorflow.python.training.adam.AdamOptimizer'>, 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, name='DDPG')[source]¶ Bases:
garage.np.algos.rl_algorithm.RLAlgorithmA DDPG model based on https://arxiv.org/pdf/1509.02971.pdf.
DDPG, also known as Deep Deterministic Policy Gradient, uses actor-critic method to optimize the policy and reward 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.
Example
$ python garage/examples/tf/ddpg_pendulum.py
Parameters: - env_spec (EnvSpec) – Environment specification.
- policy (garage.tf.policies.Policy) – Policy.
- qf (object) – The 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) – Exploration strategy.
- target_update_tau (float) – Interpolation parameter for doing the soft target update.
- policy_lr (float) – Learning rate for training policy network.
- qf_lr (float) – Learning rate for training q value network.
- discount (float) – Discount factor for the cumulative return.
- policy_weight_decay (float) – L2 regularization factor for parameters of the policy network. Value of 0 means no regularization.
- qf_weight_decay (float) – L2 regularization factor for parameters of the q value network. Value of 0 means no regularization.
- policy_optimizer (tf.Optimizer) – Optimizer for training policy network.
- qf_optimizer (tf.Optimizer) – Optimizer for training q function network.
- 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.
- name (str) – Name of the algorithm shown in computation graph.
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optimize_policy()[source]¶ Perform algorithm optimizing.
Returns: Loss of action predicted by the policy network float: Loss of q value predicted by the q network. float: ys. float: Q value predicted by the q network. Return type: float
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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