# Actor-Critic

Actions space: Discrete|Continuous

References: Asynchronous Methods for Deep Reinforcement Learning

## Network Structure

## Algorithm Description

### Choosing an action - Discrete actions

The policy network is used in order to predict action probabilites. While training, a sample is taken from a categorical distribution assigned with these probabilities. When testing, the action with the highest probability is used.

### Training the network

A batch of
transitions is used, and the advantages are calculated upon it.

Advantages can be calculated by either of the following methods (configured by the selected preset) -

1. A_VALUE - Estimating advantage directly:
   where
   is
   for each state in the batch.
2. GAE - By following the Generalized Advantage Estimation paper.

The advantages are then used in order to accumulate gradients according to

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