Minimax td-learning with neural nets in a Markov game

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 1810, Issue , 2000, Pages 117-128

  Dahl, Fredrik A ^a   Halck, Ole Martin ^a  

^a NORWEGIAN DEFENCE RESEARCH ESTABLISHMENT FFI   (Norway)

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING SYSTEMS; LINEAR PROGRAMMING; NEURAL NETWORKS; REINFORCEMENT LEARNING;

EVALUATION CRITERIA; FICTITIOUS PLAY; GAME PLAYING; MARKOV GAMES; MINIMAX-Q LEARNING; SOLVING MATRICES; TEMPORAL DIFFERENCE LEARNING; ZERO-SUM GAME;

GAME THEORY;

EID: 84974693459     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-45164-1_13     Document Type: Conference Paper

References (13)

1. Sutton, R.S.: Learning to predict by the methods of temporal differences, Machine Learning 3 (1988) 9-44.
2. Tesauro, G.J.: Practical issues in temporal difference learning, Machine Learning 8 (1992) 257-277.
3. Koller, D., Pfeffer, A.: Representations and solutions for game-theoretic problems. Artificial Intelligence 94(1) (1997) 167-215.
4. Halck, O.M., Dahl, F.A.: On classification of games and evaluation of players - with some sweeping generalizations about the literature. In: Fürnkranz, J., Kubat, M. (eds.): Proceedings of the ICML-99 Workshop on Machine Learning in Game Playing, Jozef Stefan Institute, Ljubljana (1999).
5. Billings, D., Papp, D., Schaeffer, J., Szafron, D.: Poker as a testbed for machine intelligence research. In: Mercer, R., Neufeld, E. (eds.): Advances in Artificial Intelligence, Springer- Verlag, Berlin-Heidelberg-New York (1998) 228-238.
6. Littman, M.L.: Markov games as a framework for multi-agent reinforcement learning. In: Proceedings of the 11th International Conference on Machine Learning, Morgan Kaufmann, New Brunswick (1994) 157-163.
7. Luce, R.D., Raiffa, H.: Games and Decisions. Wiley, New York (1957).
8. Strang, G.: Linear Algebra and Its Applications. Second Edition. Harcourt Brace Jovanovich, Orlando (1980).
9. Dahl, F.A., Halck, O.M.: Three games designed for the study of human and automated decision making. Definition and properties of the games Campaign, Operation Lucid and Operation Opaque. FFI/RAPPORT-98/02799, Norwegian Defence Research Establishment (FFI), Kjeller, Norway (1998).
10. Berkovitz, L.D.: The Tactical Air Game: A multimove game with mixed strategy solution. In: Grote, J.D. (ed.): The Theory and Application of Differential Games, Reidel Publishing Company, Dordrecht, The Netherlands (1975) 169-177.
11. Barto, A.G., Bradtke, S.J., Singh, S.P.: Learning to act using real-time dynamic programming. Artificial Intelligence 72 (1995) 81-138.
12. Press, W.H., Flannery, B.P., Teukolsky, S.A., Vetterling, W.T.: Numerical Recipes in C. The Art of Scientific Computing. Cambridge University Press, Cambridge, UK (1988).
13. Szepesvari, C., Littman, M.L.: A unified analysis of value-function-based reinforcementlearning algorithms. Neural Computation 11 (1999) 2017-2060.