Bayesian sparse sampling for on-line reward optimization

ICML 2005 - Proceedings of the 22nd International Conference on Machine Learning

Volumn , Issue , 2005, Pages 961-968

  Wang, Tao ^a   Lizotte, Daniel ^a   Bowling, Michael ^a   Schuurmans, Dale ^a  

^a UNIVERSITY OF ALBERTA   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTATION THEORY; DECISION MAKING; INFORMATION THEORY; SAMPLING;

BAYESIAN EXPLORATION; EXPLOITATION TRADEOFF; REINFORCEMENT LEARNING;

LEARNING SYSTEMS;

EID: 31844436266     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (29)

1. Bellman, R. (1961). Adaptive control processes. Princeton.
2. Berry, D., & Fristedt, B. (1985). Bandit problems. Chapman Hall.
3. Bertsekas, D. (1995). Dynamic programming and optimal control, vol. 2. Athena Scientific.
4. Bertsekas, D., & Tsitsiklis, J. (1996). Neuro-dynamic programming. Athena Scientific.
5. Boyan, J., & Moore, A. (1996). Learning evaluation functions for large acyclic domains. Proceedings ICML.
6. Brafman, R., &Tennenholtz, M. (2001). R-MAX - a general polynomial time algorithm for near-optimal reinforcement learning. Proceedings IJCAI.
7. Dearden, R., Friedman, N., & Andre, D. (1999). Model based Bayesian exploration. Proceedings UAL
8. Duff, M. (2002). Optimal learning: Computational procedures for Bayes-adaptive Markov decision processes. Doctoral dissertation, U. Mass.
9. Engel, Y., Manner, S., & Meir, R. (2003). Bayes meets Bellman: The Gaussian process approach to temporal difference learning. Proceedings ICML.
10. Gittins, J. (1989). Multi-armed bandit allocation indices. Wiley.
11. Jordan, M. (Ed.). (1999). Learning in graphical models. MIT Press.
12. Kaelbling, L. P. (1994). Associative reinforcement learning: Functions in k-DNF. Machine Learning, 15, 279-298.
13. Kearns, M., Mansour, Y., & Ng, A. (2001). A sparse sampling algorithm for near-optimal planning in large markov decision processes. JMLR, 1324-1331.
14. Kearns, M., & Singh, S. (1998). Near-optimal reinforcement learning in polynomial time. Proceedings ICML.
15. Lusena, C., Goldsmith, J., & Mundhenk, M. (2001). Nonapproximability results for partially observable Markov decision processes. JAIR, 14, 83-103.
16. Martin, J. (1967). Bayesian decision problems and Markov chains. Wiley.
17. Mundhenk, M., Goldsmith, J., Lusena, C., & Allender, E. (2000). Complexity of finite-horizon Markov decision processes. JACM, 47, 681-720.
18. Neal, R. (Ed.). (1996). Bayesian learning for neural networks. Springer.
19. Ng, A., & Jordan, M. (2000). Pegasus: A policy search method for large MDPs and POMDPs. Proceedings UAI.
20. Péret, L., & Garcia, F. (2004). On-line search for solving Markov decision processes via heuristic sampling. Proceedings ECAI.
21. Salganicoff, M., & Ungar, L. (1995). Active exploration and learning in real-valued spaces using multi-armed bandit allocation indices. Proceedings ICML.
22. Strens, M. (2000). A Bayesian framework for reinforcement learning. Proceedings ICML.
23. Strens, M., & Moore, A. (2002). Policy search using paired comparisons. JMLR, 3, 921-950.
24. Sutton, R., & Barto, A. (1998). Reinforcement learning: An introduction. MIT Press.
25. Thompson, W. R. (1933). On the likelihood that one unknown probability exceeds another in view of the evidence of two samples. Biometrika, 25, 285-294.
26. Watkins, C. (1989). Learning from delayed rewards. Doctoral dissertation, King's College Cambridge.
27. Wiering, M. (1999). Explorations in efficient reinforcement learning. Doctoral dissertation, Univ. Amsterdam.
28. Williams, C. (1999). Prediction with Gaussian processes. In Learning in graphical models. MIT Press.
29. Wyatt, J. (2001). Exploration control in reinforcement learning using optimistic model selection. Proc. ICML.