Reinforcement learning to play an optimal nash equilibrium in team Markov games

Advances in Neural Information Processing Systems

Volumn , Issue , 2003, Pages

  Wang, Xiaofeng ^a   Sandholm, Tuomas ^b  

^a CARNEGIE MELLON UNIVERSITY   (United States)

^b CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; GAME THEORY; MULTI AGENT SYSTEMS; REINFORCEMENT LEARNING; TELECOMMUNICATION NETWORKS;

ADAPTIVE LEARNING; CONVERGENCE CONDITIONS; LEARNING TO PLAY; MARKOV GAMES; MULTI-AGENT LEARNING; NASH EQUILIBRIA; OPTIMAL COORDINATION;

OPTIMIZATION;

EID: 27744448185     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (18)

1. C. Boutilier. Planning, learning and coordination in multi-agent decision processes. In TARK, 1996.
2. C. Claus and C. Boutilier. The dynamics of reinforcement learning in cooperative multi-agent systems. In AAAI, 1998.
3. D. Fudenberg and D.K. Levine. The theory of learning in games. MIT Press, 1998.
4. D.L. Isaacson and R.W. Madsen. Markov chain: theory and applications. John Wiley and Sons, Inc, 1976.
5. G. Weiβ. Learning to coordinate actions in multi-agent systems. In IJCAI, 1993.
6. J. Hu and W.P Wellman. Multiagent reinforcement learning: theoretical framework and an algorithm. InICML, 1998.
7. M. Kandori, G.J. Mailath, and R. Rob. Learning, mutation, and long run equilibria in games. Econometrica, 61(1):29-56, 1993.
8. M. Littman. Friend-or-Foe Q-learning in general sum game. In ICML, 2001.
9. M.L. Littman. Value-function reinforcement learning in markov games. J. of Cognitive System Research, 2:55-66, 2000.
10. M.L. Purterman. Markov decision processes-discrete stochastic dynamic programming. John Wiley, 1994.
11. M. Tan. Multiagent reinforcement learning: independent vs. cooperative agents. In ICML, 1993.
12. R.A. Howard. Dynamic programming and Markov processes. MIT Press, 1960.
13. R. Selten. Spieltheoretische behandlung eines oligopolmodells mit nachfrageträgheit. Zeitschrift für die gesamte Staatswissenschaft, 12:301-324, 1965.
14. S. Singh, T. Jaakkola, M.L. Littman, and C. Szepesvari. Convergence results for single-step on-policy reinforcement learning algorithms. Machine Learning, 2000.
15. S. Sen, M. Sekaran, and J. Hale. Learning to coordinate without sharing information. InAAAI, 1994.
16. F Thusijsman. Optimality and equilibrium in stochastic games. Centrum voor Wiskunde en Informatica, 1992.
17. T. Sandholm and R. Crites. Learning in the iterated prisoner's dilemma. Biosystems, 37:147-166, 1995.
18. H. Young. The evolution of conventions. Econometrica, 61(1):57-84, 1993.