Value-function reinforcement learning in Markov games

作者: Michael L. Littman

DOI: 10.1016/S1389-0417(01)00015-8

关键词:

摘要: … Markov games are a model of multiagent environments that are convenient for studying multiagent reinforcement learning. This paper describes a set of reinforcement-learning …

参考文章(32)
Michael P. Wellman, Junling Hu, Learning in dynamic noncooperative multiagent systems University of Michigan. ,(1999)
Jerzy Filar, Koos Vrieze, Markov Decision Processes: The Noncompetitive Case Springer, New York, NY. pp. 9- 84 ,(1997) , 10.1007/978-1-4612-4054-9_2
Michael P. Wellman, Junling Hu, Multiagent Reinforcement Learning: Theoretical Framework and an Algorithm international conference on machine learning. pp. 242- 250 ,(1998)
Michael H. Bowling, Convergence Problems of General-Sum Multiagent Reinforcement Learning international conference on machine learning. pp. 89- 94 ,(2000)
Craig Boutilier, Planning, Learning and Coordination in Multiagent Decision Processes theoretical aspects of rationality and knowledge. pp. 195- 210 ,(1996)
Michael L. Littman, Markov games as a framework for multi-agent reinforcement learning Machine Learning Proceedings 1994. pp. 157- 163 ,(1994) , 10.1016/B978-1-55860-335-6.50027-1
Michael P. Wellman, Junling Hu, Experimental Results on Q-Learning for General-Sum Stochastic Games international conference on machine learning. pp. 407- 414 ,(2000)
Robin Milner, Mathematical Centre Tracts Mathematisch Centrum. ,(1976)
Ron Sun, Dehu Qi, Rationality Assumptions and Optimality of Co-learning pacific rim international conference on multi agents. pp. 61- 75 ,(2000) , 10.1007/3-540-44594-3_5
A. G. Barto, R. S. Sutton, C. J.C.H. Watkins, Learning and Sequential Decision Making University of Massachusetts. ,(1989)