Asynchronous Deep Q-network in Continuous Environment Based on Prioritized Experience Replay

摘要：

　　Deep Q-network is a classical algorithm of reinforce learning,which is widely used and has many variants.The research content of this paper is to optimize and integrate some variant algorithms so that it has the advantage of running in the continuous environment,and improve the learning efficiency by Prioritized Experience Replay and multiple agents asynchronous parallel method,and establish the asynchronous Deep Q-network framework based on priority Experience Replay in the continuous environment.This paper uses some games in the Atari 2600 domain to test our algorithm framework,which achieved good results,improved stability,convergence speed and improved performance.

关键词： Deep Q-network Continuous Environment Prioritized Experience Replay Asynchronous

作者: Hongda Liu Hanqi Zhang Linying Gong

作者单位: College of Computer Science and Technology,Jilin University,Changchun 130012,China

会议类型: 国际会议

会议名称: 2019 2nd International Conference on Mechanical Engineering, Industrial Materials and Industrial Electronics (MEIMIE 2019)2019年第二届机械工程、工业材料和工业电子国际会议(Meimie 2019)

会议地点: 大连

会议语种:英文

页码: 472-477

在线出版日期: 2019-03-29（万方平台首次上网日期，不代表论文的发表时间）