Efficient exploration with latent structure

Robotics: Science and Systems

Volumn 1, Issue , 2005, Pages 81-88

  Leffler, Bethany R ^a   Littman, Michael L ^a   Strehl, Alexander L ^a   Walsh, Thomas J ^a  

^a RUTGERS UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 73549099066     PISSN: 23307668     EISSN: 2330765X     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (22)

1. Andrew Barto and Sridhar Mahadevan. Recent advances in hierarchical reinforcement learning. Discrete Event Systems 13, Special Issue on Reinforcement Learning 41-77, 2003.
2. [Berry and Fristedt, 1985] Donald A. Berry and Bert Fristedt. Bandit Problems: Sequential Allocation of Experiments. Chapman and Hall, London, UK, 1985.
3. [Brafman and Tennenholtz, 2002] Ronen I. Brafman and Moshe Tennenholtz. R-MAX-A general polynomial time algorithm for near-optimal reinforcement learning. Journal of Machine Learning Research, 3:213-231, 2002.
4. [Cassandra et al., 1997] Anthony Cassandra, Michael L. Littman, and Nevin L. Zhang. Incremental Pruning: A simple, fast, exact method for partially observable Markov decision processes. In Proceedings of the Thirteenth Annual Conference on Uncertainty in Artificial Intelligence (UAI-97), pages 54-61, San Francisco, CA, 1997. Morgan Kaufmann Publishers.
5. [Dearden et al., 1999] Richard Dearden, Nir Friedman, and David Andre. Model-based bayesian exploration. In Proceedings of the 15th Annual Conference on Uncertainty in Artificial Intelligence (UAI-99), pages 150-159, 1999.
6. [Dempster et al., 1977] A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society, 39(1):1-38, 1977.
7. [Even-Dar et al., 2002] Eyal Even-Dar, Shie Mannor, and Yishay Mansour. Pac bounds for multi-Armed bandit and Markov decision processes. In 15th Annual Conference on Computational Learning Theory (COLT), pages 255-270, 2002.
8. [Even-Dar et al., 2003] Eyal Even-Dar, Shie Mannor, and Yishay Mansour. Action elimination and stopping conditions for reinforcement learning. In The Twentieth International Conference on Machine Learning (ICML 2003), pages 162-169, 2003.
9. [Fiechter, 1994] Claude-Nicolas Fiechter. Efficient reinforcement learning. In Proceedings of the Seventh Annual ACM Conference on Computational Learning Theory, pages 88-97. Association of Computing Machinery, 1994.
10. [Fong, 1995] Philip W. L. Fong. A quantitative study of hypothesis selection. In Proceedings of the Twelfth International Conference on Machine Learning (ICML-95), pages 226-234, 1995.
11. [Gittins, 1989] J. C. Gittins. Multi-Armed Bandit Allocation Indices. Wiley-Interscience series in systems and optimization. Wiley, Chichester, NY, 1989.
12. [Kaelbling, 1993] Leslie Pack Kaelbling. Learning in Embedded Systems. The MIT Press, Cambridge, MA, 1993.
13. [Kearns and Koller, 1999] Michael J. Kearns and Daphne Koller. Efficient reinforcement learning in factored MDPs. In Proceedings of the 16th International Joint Conference on Artificial Intelligence (IJCAI), pages 740-747, 1999.
14. [Kearns and Singh, 2002] Michael J. Kearns and Satinder P. Singh. Nearoptimal reinforcement learning in polynomial time. Machine Learning, 49(2-3):209-232, 2002.
15. [Mannor and Tsitsiklis, 2004] Shie Mannor and John N. Tsitsiklis. The sample complexity of exploration in the multi-Armed bandit problem. Journal of Artificial Intelligence Research, 5:623-648, 2004.
16. [Puterman, 1994] Martin L. Puterman. Markov Decision Processes-Discrete Stochastic Dynamic Programming. John Wiley & Sons, Inc., New York, NY, 1994.
17. [Sherstov and Stone, 2005] Alexander A. Sherstov and Peter Stone. Improving action selection in MDP's via knowledge transfer. In Proceedings of the Twentieth National Conference on Artificial Intelligence, July 2005.
18. [Strehl and Littman, 2004] Alexander L. Strehl and Michael L. Littman. An empirical evaluation of interval estimation for Markov decision processes. In The 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI-2004), pages 128-135, 2004.
19. [Sutton and Barto, 1998] Richard S. Sutton and Andrew G. Barto. Reinforcement Learning: An Introduction. The MIT Press, 1998.
20. [Thrun, 1992] Sebastian B. Thrun. The role of exploration in learning control. In David A. White and Donald A. Sofge, editors, Handbook of Intelligent Control: Neural, Fuzzy, and Adaptive Approaches, pages 527-559. Van Nostrand Reinhold, New York, NY, 1992.
21. [Weissman et al., 2003] Tsachy Weissman, Erik Ordentlich, Gadiel Seroussi, Sergio Verdu, and Marcelo J. Weinberger. Inequalities for the L1 deviation of the empirical distribution. Technical Report HPL-2003-97R1, Hewlett-Packard Labs, 2003.
22. [Wiering and Schmidhuber, 1998] Marco Wiering and Jürgen Schmidhuber. Efficient model-based exploration. In Proceedings of the Fifth International Conference on Simulation of Adaptive Behavior (SAB'98), pages 223-228, 1998.