Segmented Actor-Critic-Advantage Architecture for Reinforcement Learning Tasks

Abstract

The article focuses on experiments with a multi module neural networks type of architecture for neuron-like machine used in reinforcing learning. This type of architecture can be used to solve complex robotic or policy optimization tasks and allows segmented storage of trained memory. Such technique speeds up the training process compared to existing actor-critical algorithms.