Lazy approximation for solving continuous finite-horizon MDPs

Proceedings of the National Conference on Artificial Intelligence

Volumn 3, Issue , 2005, Pages 1175-1180

  Li, Lihong ^a   Littman, Michael L ^a  

^a RUTGERS UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

LAZY APPROXIMATIONS; MARKOV DECISION PROCESSES (MDPS); TRANSITION MODELS;

DECISION THEORY; MARKOV PROCESSES; PROBLEM SOLVING; REAL TIME SYSTEMS; STATE SPACE METHODS;

APPROXIMATION THEORY;

EID: 29344431927     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (14)

1. Baird, L. 1995. Residual algorithms: Reinforcement learning with function approximation. In Proceedings of the Twelfth International Conference on Machine Learning (ICML-95), 30-37.
2. Bellman, R. 1957. Dynamic Programming. Princeton University Press.
3. Bertsekas, D. P., and Tsitsiklis, J. N. 1996. Neuro-Dynamic Programming. Athena Scientific.
4. Boyan, J. A., and Littman, M. L. 2001. Exact solutions to time-dependent MDPs. In Advances in Neural Information Processing Systems 13 (NIPS-00), 1026-1032.
5. Boyan, J. A.; Moore, A. W.; and Sutton, R. S., eds. 1995. Proceedings of the ICML-95 Workshop on Value Function Approximation.
6. Bresina, J. L.; Dearden, R.; Meuleau, N.; Ramkrishnan, S.; Smith, D. E.; and Washington, R. 2002. Planning under continuous time and resource uncertainty: A challenge for AI. In Proceedings of the Eighteenth Conference on Uncertainty in Artificial Intelligence (UAI-02), 77-84.
7. Feng, Z.; Dearden, R.; Meuleau, N.; and Washington, R. 2004. Dynamic programming for structured continuous Markov decision problems. In Proceedings of the Twentieth Conference on Uncertainty in Artificial Intelligence (UAI-04), 154-161.
8. Fikes, R., and Nilsson, N. J. 1971. STRIPS: A new approach to the application of theorem proving to problem solving. Artificial Intelligence 2(3-4):189-208.
9. Friedman, J. H.; Bentley, J. L.; and Finkel, R. A. 1977. An algorithm for finding best matches in logarithmic expected time. ACM Transactions on Mathematical Software 3(3):209-226.
10. Li, L., and Littman, M. L. 2005. Lazy approximation: A new approach for solving continuous finite-horizon MDPs. Technical Report 577, Department of Computer Science, Rutgers University.
11. Munos, R., and Moore, A. W. 2002. Variable resolution discretization in optimal control. Machine Learning 49(2-3):291-323.
12. Puterman, M. L. 1994. Markov Decision Processes: Discrete Stochastic Dynamic Programming. New York: Wiley-Interscience.
13. Singh, S. P., and Yee, R. C. 1994. An upper bound on the loss from approximate optimal-value functions. Machine Learning 16(3):227-233.
14. Sutton, R. S., and Barto, A. G. 1998. Reinforcement Learning: An Introduction. Cambridge, MA: MIT Press.