Combining online and offline knowledge in UCT

ACM International Conference Proceeding Series

Volumn 227, Issue , 2007, Pages 273-280

  Gelly, Sylvain ^a   Silver, David ^b  

^a UNIVERSITÉ PARIS SACLAY   (France)

^b UNIVERSITY OF ALBERTA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SIMULATION; FUNCTIONAL ANALYSIS; MONTE CARLO METHODS; ONLINE SYSTEMS; RANDOM PROCESSES;

OFFLINE VALUE FUNCTION; SIMULATION POLICY; VALUE FUNCTIONS;

LEARNING ALGORITHMS;

EID: 34547990649     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1273496.1273531     Document Type: Conference Paper

References (16)

1. Auer, P., Cesa-Bianchi, N., & Fischer, P. (2002). Finite-time analysis of the multi-armed bandit problem. Machine Learning, 47, 235-256.
2. Baxter, J., Tridgell, A., & Weaver, L. (1998). Experiments in parameter learning using temporal differences. International Computer Chess Association Journal, 21, 84-99.
3. Bruegmann, B. (1993). Monte-Carlo Go. http://www.cgl.ucsf.edu/go/ Programs/Gobble.html.
4. Buro, M. (1999). From simple features to sophisticated evaluation functions. 1st International Conference on Computers and Games (pp. 126-145).
5. Coulom, R. (2006). Efficient selectivity and backup operators in Monte-Carlo tree search. 5th International Conference on Computer and Games, 2006-05-29. Turin, Italy.
6. Enzenberger, M. (2003). Evaluation in Go by a neural network using soft segmentation. 10th Advances in Computer Games Conference (pp. 97-108).
7. Gelly, S., Wang, Y., Munos, R., & Teytaud, O. (2006). Modification of UCT with patterns in Monte-Carlo Go (Technical Report 6062). INRIA.
8. Kocsis, L., & Szepesvari, C. (2006). Bandit based Monte-Carlo planning. 15th European Conference on Machine Learning (pp. 282-293).
9. Schaeffer, J., Hlynka, M., & Jussila, V. (2001). Temporal difference learning applied to a high-performance game-playing program. 17th International Joint Conference on Artificial Intelligence (pp. 529-534).
10. Schraudolph, N., Dayan, P., & Sejnowski, T. (1994). Temporal difference learning of position evaluation in the game of Go. Advances in Neural Information Processing Systems 6 (pp. 817-824). San Francisco: Morgan Kaufmann.
11. 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).
12. Sutton, R. (1988). Learning to predict by the method of temporal differences. Machine Learning, 3, 9-44.
13. Sutton, R. (1990). Integrated architectures for learning, planning, and reacting based on approximating dynamic programming. 7th International Conference on Machine Learning (pp. 216-224).
14. Sutton, R. (1996). Generalization in reinforcement learning: Successful examples using sparse coarse coding. Advances in Neural Information Processing Systems 8 (pp. 1038-1044).
15. Sutton, R., & Barto, A. (1998). Reinforcement learning: An introduction. Cambridge, MA: MIT Press.
16. Wang, Y., & Gelly, S. (2007). Modifications of UCT and sequence-like simulations for Monte-Carlo Go. IEEE Symposium on Computational Intelligence and Games, Honolulu, Hawaii (pp. 175-182).