Efficient reinforcement learning in factored MDPs

IJCAI International Joint Conference on Artificial Intelligence

Volumn 2, Issue , 1999, Pages 740-747

  Kearns, Michael ^a   Koller, Daphne ^b  

^a AT AND T LABS RESEARCH   (United States)

^b STANFORD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DYNAMIC BAYESIAN NETWORKS; GLOBAL STATE; GRAPHICAL STRUCTURES; MARKOV DECISION PROCESSES; NEAR-OPTIMAL ALGORITHMS; RUNNING TIME; TRANSITION MODEL;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; MARKOV PROCESSES; REINFORCEMENT LEARNING;

PARAMETER ESTIMATION;

EID: 84880677563     PISSN: 10450823     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (13)

1. C. Boutilier, T. Dean, and S. Hanks. Decision theoretic planning: Structural assumptions and computational leverage. Journal of Artificial Intelligence Research, 1999. To appear.
2. X. Boyen and D. Koller. Tractable inference for complex stochastic processes. In Proc. UAI, pages 33-42,1998.
3. R.L. Dobrushin. Central limit theorem for nonstationary Markov chains. Theory of Probability and its Applications, pages 65-80,1956.
4. J. Forbes, T. Huang, K. Kanazawa, and S.J. Russell. Hie BATmobile: Towards a Bayesian automated taxi. In Proc. IJCAI, 1995.
5. D. Heckerman. A tutorial on learning with Bayesian networks. Technical Report MSR-TR-95-06, Microsoft Research, 1995.
6. R. A. Howard and J. E. Matheson. Influence diagrams. In R. A. Howard and J. E. Matheson, editors, Readings on the Principles and Applications of Decision Analysis, pages 721-762. Strategic Decisions Group, Menlo Park, California, 1984.
7. F.V. Jensen, U. Kjarulff, K.G. Olesen, and J. Pedersen. An expert system for control of waste water treatment - a pilot project. Technical report, Judex Datasystemer A/S, Aalborg, 1989. In Danish.
8. M. Jerrum and A. Sinclair. Polynomial-time approximation algorithms for the Ising model. SIAM Journal on Computing, 22:1087-1116,1993.
9. M. Keams and SP. Singh. Near-optimal performance for reinforcement learning in polynomial time. In Proc. ICML, pages 260-268,1998.
10. M. Kearns, Y Mansour, and A. Ng. A sparse sampling algorithm for near-optimal planning in large markov decision processes. In these proceedings, 1999.
11. D. Koller and R. Parr. Computing factored value functions for policies in structured MDPs. In these proceedings, 1999.
12. T. Undvall. Lectureson the Coupling Method. Wiley, 1992.
13. N. Meuleau, M. Hauskrecht, K-E. Kirn, L. Peshkin, L.P. Kaelbling, T. Dean, and C. Boutilier. Solving very large weakly coupled Markov decision processes. In Proc. AAA1, pages 165-172,1998.