Model-based exploration in continuous state spaces

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4612 LNAI, Issue , 2007, Pages 258-272

  Jong, Nicholas K ^a   Stone, Peter ^a  

^a UNIVERSITY OF TEXAS AT AUSTIN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DATA REDUCTION; MATHEMATICAL MODELS; PROBLEM SOLVING; STATE SPACE METHODS;

CONTINUOUS STATE SPACES; CONTROLLED DYNAMICAL SYSTEM; HUMAN DOMAIN KNOWLEDGE; MODEL-BASED EXPLORATION;

LEARNING ALGORITHMS;

EID: 34548083538     PISSN: 03029743     EISSN: 16113349     Source Type: Conference Proceeding    
DOI: 10.1007/978-3-540-73580-9_21     Document Type: Conference Paper

References (13)

1. Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)
2. Watkins, C.: Learning From Delayed Rewards. PhD thesis, University of Cambridge (1989)
3. Kearns, M., Singh, S.: Near-optimal reinforcement learning in polynomial time. In: Proceedings of the Fifteenth International Conference on Machine Learning, pp. 260-268 (1998)
4. Atkeson, C.G., Moore, A.W., Schaal, S.: Locally weighted learning for control. Artificial Intelligence Review 11, 75-113 (1997)
5. Lagoudakis, M.G., Parr, R.: Least-squares policy iteration. Journal of Machine Learning Research 4, 1107-1149 (2003)
6. Gordon, G.J.: Stable function approximation in dynamic programming. In: Proceedings of the Twelfth International Conference on Machine Learning (1995)
7. Brafman, R.I., Tennenholtz, M.: R-max - a general polynomial time algorithm for near-optimal reinforcement learning. Journal of Machine Learning Research 3, 213-231 (2002)
8. Puterman, M.L.: Markov Decision Processes: Discrete Stochastic Dynamic Programming. John Wiley & Sons, Inc., West Sussex, England (1994)
9. Moore, A.W., Atkeson, C.G.: Prioritized sweeping: Reinforcement learning with less data and less real time. Machine Learning 13, 103-130 (1993)
10. Littman, M.L., Dean, T.L., Kaelbling, L.P.: On the complexity of solving Markov decision problems. In: Proceedings of the Eleventh Conference on Uncertainty in Artificial Intelligence (1995)
11. Kekade, S.M.: On the Sample Complexity of Reinforcement Learning. PhD thesis, University College London (2003)
12. Dutech, A., Edmunds, T., Kok, J., Lagoudakis, M., Littman, M., Riedmiller, M., Russell, B., Scherrer, B., Sutton, R., Timmer, S., Vlassis, N., White, A., Whiteson, S.: Reinforcement learning benchmarks and bake-offs II (2005) http:// www.cs.rutgers.edu/~mlittman/topics/nips05-mdp/bakeoffs05.pdf
13. Sutton, R.S.: Generalization in reinforcement learning: Successful examples using sparse coarse coding. Advances in Neural Information Processing Systems 8 (1996)