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

页码：3190 / 3194

页数：5

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