Research on Game-Playing Agents Based on Deep Reinforcement Learning

Robotics

Kai Zhao¹  Yuxie Luo¹  Jia Song¹  Yang Liu² 

[1] School of Astronautics, Beihang University (BUAA), Beijing 100191, China;School of Automation Science and Electrical Engineering, Beihang University (BUAA), Beijing 100191, China;

关键词: deep reinforcement learning (DRL);    deep deterministic policy gradient (DDPG);    dynamic path planning;    confrontation environment;   

DOI  :  10.3390/robotics11020035

来源: DOAJ

【 摘 要 】

Path planning is a key technology for the autonomous mobility of intelligent robots. However, there are few studies on how to carry out path planning in real time under the confrontation environment. Therefore, based on the deep deterministic policy gradient (DDPG) algorithm, this paper designs the reward function and adopts the incremental training and reward compensation method to improve the training efficiency and obtain the penetration strategy. The Monte Carlo experiment results show that the algorithm can effectively avoid static obstacles, break through the interception, and finally reach the target area. Moreover, the algorithm is also validated in the Webots simulator.

【 授权许可】

Unknown