Characterizing Markov decision processes

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

Volumn 2430, Issue , 2002, Pages 391-404

  Ratitch, Bohdana ^a   Precup, Doina ^a  

^a MCGILL UNIVERSITY   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATION ALGORITHMS; MACHINE LEARNING; MARKOV PROCESSES; REINFORCEMENT LEARNING;

FUNCTION APPROXIMATION; MARKOV DECISION PROCESSES; MEASUREMENTS OF; OPTIMIZATION PROBLEMS; QUALITY OF LEARNING;

LEARNING ALGORITHMS;

EID: 84945289483     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-36755-1_33     Document Type: Conference Paper

References (17)

1. Bertsekas, D. P., Tsitsiklis, J. N.: Neuro-Dynamic Programming. Belmont, MA: Athena Scientific (1996)
2. Cohen, P. R.: Empirical Methods for Artificial Intelligence. Cambridge, MA: The MIT Press (1995)
3. Dean, T., Kaelbling, L., Kirman, J., Nicholson, A.: Planning under Time Constraints in Stochastic Domains. Artificial Intelligence 76(1-2) (1995) 35-74
4. Dearden, R., Friedman, N., Andre, D.: Model-Based Bayesian Exploration. In Uncertainty in Artificial Intelligence: Proceedings of the Fifteenth Conference (UAI-1999) 150-159
5. Gordon, J. G.: Reinforcement Learning with Function Approximation Converges to a Region. Advances in Neural Information Processing Systems 13 (2001) 1040-1046
6. Hogg, T., Huberman, B. A., Williams, C. P.: Phase Transitions and the Search Problem (Editorial). Artificial Intelligence, 81 (1996) 1-16
7. Hoos, H. H., Stutzle, T.: Local Search Algorithms for SAT: An Empirical Evaluation. Journal of Automated Reasoning, 24 (2000) 421-481.
8. Kearns, M., Singh, S.: Near-Optimal Reinforcement Learning in Polynomial Time. In Proceedings of the 15th International Conference on Machine Learning (1998) 260-268
9. Kirman, J.: Predicting Real-Time Planner Performance by Domain Characterization. Ph.D. Thesis, Brown University (1995)
10. Lagoudakis, M., Littman, M. L.: Algorithm Selection using Reinforcement Learning Proceedings of the 17th International Conference on Machine Learning (2000) 511-518
11. Meuleau, N., Bourgine, P.: Exploration of Multi-State Environments: Local Measures and Back-Propagation of Uncertainty. Machine Learning 35(2) (1999) 117-154
12. Moore, A. W., Atkeson, C. G.: Prioritized Sweeping: Reinforcement Learning with Less Data and Less Time. Machine Learning, 13 (1993) 103-130
13. Papadimitriou, C. H., Steiglitz, K: Combinatorial Optimization: Algorithms and Complexity. Prentice Hall (1982)
14. Papadimitriou, C. H., Tsitsiklis, J. N.: The Complexity of Markov Chain Decision Processes. Mathematics of Operations Research 12(3) (1987) 441-450
15. Puterman, M. L.: Markov Decision Processes: Discrete Stochastic Dynamic Programming. Wiley (1994)
16. Singh, S. P., Jaakkola, T., Jordan, M. I.: Reinforcement Learning with Soft State Aggregation. Advances in Neural Information Processing Systems, 7 (1995) 361-368
17. Sutton, R. S., Barto, A. G.: Reinforcement Learning. An Introduction. Cambridge, MA: The MIT Press (1998)