Multi-objective Reinforcement Learning Algorithm for MOSDMP in Unknown Environment

被引：12
|
作者：
Zhao, Yun [1 ]
Chen, Qingwei [1 ]
Hu, Weili [1 ]
机构：
[1] Univ Sci & Technol, Dept Automat, Nanjing, Jiangsu Provinc, Peoples R China
来源：
2010 8TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA) | 2010年
关键词：
Reinforcement learning; Markov decision processes (MDP); Fuzzy inference system; Action decision; MARKOV DECISION-PROCESSES;
D O I：
10.1109/WCICA.2010.5553980
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 new multi-objective reinforcement learning algorithm for multi-objective sequential decision making problems in unknown environment is proposed. The salient characters of the algorithm are: 1) decision maker's objective preference is introduced to guide learning direction; 2) a new measure of comparing action decisions under several objectives based on the fuzzy inference system is defined; 3) fast learning speed can be achieved. Simulation results demonstrate that the proposed algorithm has a good learning performance.
引用
收藏
页码：3190 / 3194
页数：5
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