Evolutionary game theory and multi-agent reinforcement learning

被引:78
作者
Tuyls, K [1 ]
Nowé, A
机构
[1] Univ Maastricht, Inst Knowledge & Agent Technol, Maastricht, Netherlands
[2] Univ Virginia, Computat Modeling Lab, Brussels, Belgium
关键词
D O I
10.1017/S026988890500041X
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
In this paper we survey the basics of reinforcement learning and (evolutionary) game theory, applied to the field of multi-agent systems. This paper contains three parts. We start with an overview on the fundamentals of reinforcement learning. Next we summarize the most important aspects of evolutionary game theory. Finally, we discuss the state-of-the-art of multi-agent reinforcement learning and the mathematical connection with evolutionary game theory.
引用
收藏
页码:63 / 90
页数:28
相关论文
共 61 条
[1]  
Anderson C. W., 1987, Proceedings of the Fourth International Workshop on Machine Learning, P103
[2]  
[Anonymous], 1974, Differential Equations, Dynamical Systems, and Linear Algebra
[3]  
[Anonymous], 1999, MULTIAGENT REINFORCE
[4]  
[Anonymous], 1998, Evol. Games Popul. Dyn., DOI DOI 10.1017/CBO9781139173179
[5]   NEURONLIKE ADAPTIVE ELEMENTS THAT CAN SOLVE DIFFICULT LEARNING CONTROL-PROBLEMS [J].
BARTO, AG ;
SUTTON, RS ;
ANDERSON, CW .
IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS, 1983, 13 (05) :834-846
[6]  
Bazzan AL, 2003, P 1 INT WORKSH EV GA
[7]  
BAZZAN AL, 1997, THESIS U KARLSRUHE
[8]  
Bellman RE., 1962, Applied dynamic programming
[9]  
Bertsekas D. P., 1976, MATH SCI ENG, V125
[10]  
BJORNERSTEDT J, 1995, RATIONAL FDN EC BEHA