# 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
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:
Use the next states as the inputs to the target network and extract the
value, from within the head, to get
. Then, update the online network using the current states and actions as inputs, and
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.

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