Deep Deterministic Policy Gradient (DDPG) implementation in TensorFlow.

class DDPG(env_spec, policy, qf, replay_buffer, sampler, *, steps_per_epoch=20, n_train_steps=50, buffer_batch_size=64, min_buffer_size=int(10000.0), max_episode_length_eval=None, exploration_policy=None, target_update_tau=0.01, discount=0.99, policy_weight_decay=0, qf_weight_decay=0, policy_optimizer=tf.compat.v1.train.AdamOptimizer, qf_optimizer=tf.compat.v1.train.AdamOptimizer, policy_lr=_Default(0.0001), qf_lr=_Default(0.001), clip_pos_returns=False, clip_return=np.inf, max_action=None, reward_scale=1.0, name='DDPG')


Inheritance diagram of

A DDPG model based on

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.


$ python garage/examples/tf/

  • env_spec (EnvSpec) – Environment specification.

  • policy (Policy) – Policy.

  • qf (object) – The q value network.

  • replay_buffer (ReplayBuffer) – Replay buffer.

  • sampler (garage.sampler.Sampler) – Sampler.

  • steps_per_epoch (int) – Number of train_once calls per epoch.

  • n_train_steps (int) – Training steps.

  • buffer_batch_size (int) – Batch size of replay buffer.

  • max_episode_length_eval (int or None) – Maximum length of episodes used for off-policy evaluation. If None, defaults to env_spec.max_episode_length.

  • min_buffer_size (int) – The minimum buffer size for replay buffer.

  • exploration_policy (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.compat.v1.train.Optimizer) – Optimizer for training policy network.

  • qf_optimizer (tf.compat.v1.train.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.

  • name (str) – Name of the algorithm shown in computation graph.


Obtain samplers and start actual training for each epoch.


trainer (Trainer) – Experiment trainer, which provides services such as snapshotting and sampler control.


The average return in last epoch cycle.

Return type