# Normalized Advantage Functions

Actions space: Continuous

References: Continuous Deep Q-Learning with Model-based Acceleration

## Network Structure

## Algorithm Description

### Choosing an action

The current state is used as an input to the network. The action mean \(\mu(s_t )\) is extracted from the output head. It is then passed to the exploration policy which adds noise in order to encourage exploration.

### Training the network

The network is trained by using the following targets: \(y_t=r(s_t,a_t )+\gamma\cdot V(s_{t+1})\) Use the next states as the inputs to the target network and extract the \(V\) value, from within the head, to get \(V(s_{t+1} )\). Then, update the online network using the current states and actions as inputs, and \(y_t\) as the targets. After every training step, use a soft update in order to copy the weights from the online network to the target network.

class `rl_coach.agents.naf_agent.``NAFAlgorithmParameters`