Monte-Carlo simulation balancing

Proceedings of the 26th International Conference On Machine Learning, ICML 2009

Volumn , Issue , 2009, Pages 945-952

  Silver, David ^a   Tesauro, Gerald ^b  

^a UNIVERSITY OF ALBERTA   (Canada)

^b IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

INTELLIGENT SYSTEMS; MEAN SQUARE ERROR; MONTE CARLO METHODS; OPTIMIZATION; REINFORCEMENT LEARNING;

COMPUTER GO; GRADIENT DESCENT; MEAN SQUARED ERROR; POLICY GRADIENT;

LEARNING ALGORITHMS;

EID: 71149102015     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (11)

1. Billings, D., Castillo, L., Schaeffer, J., & Szafron, D. (1999). Using probabilistic knowledge and simulation to play poker. Proceedings of the 16th National Conference on Artificial Intelligence (pp. 697-703).
2. Chaslot, G., Winands, M., Szita, I., & van den Herik, H. (2008). Parameter tuning by the cross-entropy method. 8th European Workshop on Reinforcement Learning.
3. Coulom, R. (2007). Computing Elo ratings of move patterns in the game of Go. Computer Games Workshop.
4. Finnsson, H., & Björnsson, Y. (2008). Simulation-based approach to general game playing. 23rd Conference on Artificial Intelligence (pp. 259-264).
5. Gelly, S., & Silver, D. (2007). Combining online and offline learning in UCT. 17th International Conference on Machine Learning (pp. 273-280).
6. Gelly, S., Wang, Y., Munos, R., & Teytaud, O. (2006). Modification of UCT with patterns in Monte-Carlo Go (Technical Report 6062). INRIA.
7. Kocsis, L., & Szepesvari, C. (2006). Bandit based Monte-Carlo planning. 15th European Conference on Machine Learning (pp. 282-293).
8. Sheppard, B. (2002). World-championship-caliber Scrabble. Artificial Intelligence, 134, 241-275.
9. Silver, D., Sutton, R., & Müller, M. (2007). Reinforcement learning of local shape in the game of Go. 20th International Joint Conference on Artificial Intelligence (pp. 1053-1058).
10. Tesauro, G., & Galperin, G. (1996). On-line policy improvement using Monte-Carlo search. Advances in Neural Information Processing 9 (pp. 1068-1074).
11. Williams, R. J. (1992). Simple statistical gradient-following algorithms for connectionist reinforcement learning. Machine Learning, 8, 229-256.