A Review of Deep Reinforcement Learning Algorithms and Comparative Results on Inverted Pendulum System

Abstract

The control of inverted pendulum problem that is one of the classical control problems is important for many areas from autonomous vehicles to robotic. This chapter presents the usage of the deep reinforcement learning algorithms to control the cart-pole balancing problem. The first part of the chapter reviews the theories of deep reinforcement learning methods such as Deep Q Networks (DQN), DQN with Prioritized Experience Replay (DQN+PER), Double DQN (DDQN), Double Dueling Deep-Q Network (D3QN), Reinforce, Asynchronous Advanced Actor Critic Asynchronous (A3C) and Synchronous Advantage Actor-Critic (A2C). Then, the cart-pole balancing problem in OpenAI Gym environment is considered to implement the deep reinforcement learning methods. Finally, the performance of all methods are comparatively given on the cart-pole balancing problem. The results are presented by tables and figures. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.