# Deep Deterministic Policy Gradient — Reinforcement Learning Coach 0.12.0 documentation

# Deep Deterministic Policy Gradient

Actions space: Continuous

References: Continuous control with deep reinforcement learning

## Network Structure

## Algorithm Description

### Choosing an action

Pass the current states through the actor network, and get an action mean vector \(\mu\). While in training phase, use a continuous exploration policy, such as the Ornstein-Uhlenbeck process, to add exploration noise to the action. When testing, use the mean vector \(\mu\) as-is.

### Training the network

Start by sampling a batch of transitions from the experience replay.

- To train the critic network, use the following targets:

  \(y_t=r(s_t,a_t )+\gamma \cdot Q(s_{t+1},\mu(s_{t+1} ))\)

  First run the actor target network, using the next states as the inputs, and get \(\mu (s_{t+1} )\). Next, run the critic target network using the next states and \(\mu (s_{t+1} )\), and use the output to calculate \(y_t\) according to the equation above. To train the network, use the current states and actions as the inputs, and \(y_t\) as the targets.

- To train the actor network, use the following equation:

  \(\nabla_{\theta^\mu } J \approx E_{s_t \tilde{} \rho^\beta } [\nabla_a Q(s,a)|_{s=s_t,a=\mu (s_t ) } \cdot \nabla_{\theta^\mu} \mu(s)|_{s=s_t} ]\)

  Use the actor’s online network to get the action mean values using the current states as the inputs. Then, use the critic online network in order to get the gradients of the critic output with respect to the action mean values \(\nabla _a Q(s,a)|_{s=s_t,a=\mu(s_t ) }\). Using the chain rule, calculate the gradients of the actor’s output, with respect to the actor weights, given \(\nabla_a Q(s,a)\). Finally, apply those gradients to the actor network.

After every training step, do a soft update of the critic and actor target networks’ weights from the online networks.

class `rl_coach.agents.ddpg_agent.``DDPGAlgorithmParameters`
  Parameters

    - num_steps_between_copying_online_weights_to_target – (StepMethod) The number of steps between copying the online network weights to the target network weights.

    - rate_for_copying_weights_to_target – (float) When copying the online network weights to the target network weights, a soft update will be used, which weight the new online network weights by rate_for_copying_weights_to_target

    - num_consecutive_playing_steps – (StepMethod) The number of consecutive steps to act between every two training iterations

    - use_target_network_for_evaluation – (bool) If set to True, the target network will be used for predicting the actions when choosing actions to act. Since the target network weights change more slowly, the predicted actions will be more consistent.

    - action_penalty – (float) The amount by which to penalize the network on high action feature (pre-activation) values. This can prevent the actions features from saturating the TanH activation function, and therefore prevent the gradients from becoming very low.

    - clip_critic_targets – (Tuple[float, float] or None) The range to clip the critic target to in order to prevent overestimation of the action values.

    - use_non_zero_discount_for_terminal_states – (bool) If set to True, the discount factor will be used for terminal states to bootstrap the next predicted state values. If set to False, the terminal states reward will be taken as the target return for the network.