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