A Reinforcement Learning based End-to-End Algorithm for Confrontation Problem

被引：0
|
作者：
Wang, Siqiang [1 ]
Yao, Haodi [1 ]
Yao, Yu [1 ]
He, Fenghua [1 ]
机构：
[1] Harbin Inst Technol, Harbin 150001, Peoples R China
来源：
PROCEEDINGS OF THE 38TH CHINESE CONTROL CONFERENCE (CCC) | 2019年
关键词：
Reinforcement Learning; Confrontation; Incomplete Information; Finite State Machine; GAME;
D O I：
10.23919/chicc.2019.8866337
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, a confrontation problem between two agents is investigated in which one agent is required to find then eliminate the other agent through projecting bullets. A reinforcement learning based algorithm is designed to realize an end-to-end strategy for one agent to confront the other. Self-play method is used to enhance the algorithm. Simulation results show the effectiveness of the proposed algorithm in confrontation with FSM based strategy and human player.
引用
收藏
页码：2594 / 2598
页数：5
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