Generalized sampling and variance in counterfactual regret minimization

Proceedings of the National Conference on Artificial Intelligence

Volumn 2, Issue , 2012, Pages 1355-1361

  Gibson, Richard ^a   Lanctot, Marc ^a   Burch, Neil ^a   Szafron, Duane ^a   Bowling, Michael ^a  

^a UNIVERSITY OF ALBERTA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

CONVERGENCE RATES; EXTENSIVE-FORM GAMES; IMPERFECT INFORMATION; ITERATIVE ALGORITHM; MONTE CARLO; NASH EQUILIBRIA; REGRET MINIMIZATION; SAMPLED VALUES; SAMPLING ALGORITHM; SAMPLING SCHEMES; TEXAS HOLD'EM; TIME COST; TREE TRAVERSAL; UNBIASED ESTIMATOR;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; FORESTRY; ITERATIVE METHODS; TREES (MATHEMATICS);

ESTIMATION;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; ESTIMATION; ITERATIVE METHODS; OPTIMIZATION;

EID: 84868284212     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (8)

1. Gibson, R.; Lanctot, M.; Burch, N.; Szafron, D.; and Bowling, M. 2012. Generalized sampling and variance in counterfactual regret minimization. Technical Report TR12-02, University of Alberta.
2. Gilpin, A., and Sandholm, T. 2006. A competitive Texas Hold'em poker player via automated abstraction and real-time equilibrium computation. In Twenty-First Conference on Artificial Intelligence (AAAI), 1007-1013.
3. Hart, S., and Mas-Colell, A. 2000. A simple adaptive procedure leading to correlated equilibrium. Econometrica 68:1127-1150.
4. Hoda, S.; Gilpin, A.; Peña, J.; and Sandholm, T. 2010. Smoothing techniques for computing Nash equilibria of sequential games. Mathematics of Operations Research 35(2):494-512.
5. Koller, D.; Megiddo, N.; and von Stengel, B. 1994. Fast algorithms for finding randomized strategies in game trees. In Annual ACM Symposium on Theory of Computing (STOC'94), 750-759.
6. Lanctot, M.; Waugh, K.; Zinkevich, M.; and Bowling, M. 2009a. Monte Carlo sampling for regret minimization in extensive games. In Advances in Neural Information Processing Systems 22 (NIPS), 1078-1086.
7. Lanctot, M.; Waugh, K.; Zinkevich, M.; and Bowling, M. 2009b. Monte Carlo sampling for regret minimization in extensive games. Technical Report TR09-15, University of Alberta.
8. Zinkevich, M.; Johanson, M.; Bowling, M.; and Piccione, C. 2008. Regret minimization in games with incomplete information. In Advances in Neural Information Processing Systems 20 (NIPS), 905-912.