Survey of Deep Q-Network variants in PyGame Learning Environment

Abstract

Q-value function models based on variations of Deep Q-Network (DQN) have shown good results in many virtual environments. In this paper, over 30 sub-algorithms were surveyed that influence the performance of DQN variants. Important stability and repeatability aspects of state of art Deep Reinforcement Learning algorithms were found. Multi Deep Q-Network (MDQN) as a generalization of popular Double Deep Q-Network (DDQN) algorithm was developed. Visual representations of a learning process as Q-Value maps were produced using PyGame Learning Environment. Videos of trained models available in following link: http://yellowrobot.xyz/mdqn