Polynomial-time reinforcement learning of near-optimal policies

Proceedings of the National Conference on Artificial Intelligence

Volumn , Issue , 2002, Pages 205-210

  Pivazyan, Karèn ^a   Shoham, Yoav ^a  

^a Stanford University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; COMPUTATIONAL COMPLEXITY; DECISION THEORY; GAME THEORY; MARKOV PROCESSES; POLYNOMIALS; THEOREM PROVING;

REINFORCEMENT LEARNING;

LEARNING SYSTEMS;

EID: 0036923099     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (8)

1. Bertsekas, D. P., and Tsitsiklis, J. N. 1996. Neuro-Dynamic Programming. Belmont, MA: Athena Scientific.
2. Bertsekas, D. P. 1995. Dynamic Programming and Optimal Control, Vols. I and II. Belmont, MA: Athena Scientific.
3. Brafman, R., and Tennenholtz, M. 2001. R-max: A general polynomial time algorithm for Near-Optimal reinforcement learning. In Proc. of the 17th International Conf. on Artificial Intelligence (IJCAI-01), 953-958. San Francisco, CA: Morgan Kaufmann Publishers, Inc.
4. Fiechter, C.-N. 1994. Efficient reinforcement learning. In Proc. of the 7th Annual ACM Conf. on Computational Learning Theory, 88-97. New Brunswick, New Jersey: ACM Press.
5. Fiechter, C.-N. 1997. Expected mistake bound model for on-line reinforcement learning. In Proc. of the 14th International Conf. on Machine Learning, 116-124.
6. Filar, J., and Vrieze, K. 1997. Competitive Markov Decision Processes. New York, NY: Springer-Verlag.
7. Kearns, M., and Singh, S. 1998. Near-optimal reinforcement learning in polynomial time. In Proc. 15th International Conf. on Machine Learning, 260-268. Morgan Kaufmann, San Francisco, CA.
8. Wal, J. V. D. 1981. Stochastic Dynamic Programming, volume 139 of Mathematical Center Tracts.