# Normalized Advantage Functions

**Actions space:** Continuous

**References:** [Continuous Deep Q-Learning with Model-based Acceleration](https://arxiv.org/abs/1603.00748.pdf)

## Network Structure

<p style="text-align: center;">

<img src="..\..\design_imgs\naf.png" width=600>

</p>

## Algorithmic 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.
