Efficient learning equilibrium

Advances in Neural Information Processing Systems

Volumn , Issue , 2003, Pages

  Brafman, Ronen I ^a   Tennenholtz, Moshe ^b  

^a BEN GURION UNIVERSITY OF THE NEGEV   (Israel)

^b Stanford University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

GAME THEORY; POLYNOMIAL APPROXIMATION;

EFFICIENT LEARNING; IMPERFECT MONITORING; NASH EQUILIBRIA; NON-COOPERATIVE; POLYNOMIAL-TIME; STOCHASTIC GAME;

LEARNING ALGORITHMS;

EID: 32844472184     PISSN: 10495258     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (10)

1. R. I. Brafman and M. Tennenholtz. R-max - a general polynomial time algorithm for near-optimal reinforcement learning. In IJCAI'01, 2001.
2. R. I. Brafman and M. Tennenholtz. Efficient learning equilibrium. Technical Report 02-06, Dept. of Computer Science, Ben-Gurion University, 2002.
3. C. Claus and C. Boutilier. The dynamics of reinforcement learning in cooperative multi-agent systems. In Proc. Workshop on Multi-Agent Learning, pages 602-608, 1997.
4. I. Erev and A.E. Roth. Predicting how people play games: Reinforcement learning in games with unique strategy equilibrium. American Economic Review, 88:848-881, 1998.
5. D. Fudenberg and D. Levine. The theory of learning in games. MIT Press, 1998.
6. D. Fudenberg and J. Tirole. Game Theory. MIT Press, 1991.
7. J. Hu and M.P. Wellman. Multi-agent reinforcement learning: Theoretical framework and an algorithms. In Proc. 15th ICML, 1998.
8. L. P. Kaelbling, M. L. Littman, and A. W. Moore. Reinforcement learning: A survey. Journal of AI Research, 4:237-285, 1996.
9. M. L. Littman. Markov games as a framework for multi-agent reinforcement learning. In Proc. 11th ICML, pages 157-163, 1994.
10. L.S. Shapley. Stochastic Games. In Proc. Nat. Acad. Scie. USA, Volume 39, pages 1095-1100, 1953.