Optimistic Linear Programming gives logarithmic regret for irreducible MDPs

Advances in Neural Information Processing Systems 20 - Proceedings of the 2007 Conference

Volumn , Issue , 2008, Pages

  Tewari, Ambuj ^a   Bartlett, Peter L ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; MARKOV PROCESSES;

AVERAGE REWARD; LINEAR PROGRAMS; LINEAR-PROGRAMMING; MARKOV DECISION PROCESSES; OPTIMAL POLICIES; OPTIMISTICS; REGRET BOUNDS; SIMPLE++; STATE TRANSITION PROBABILITIES; TRANSITION PROBABILITIES;

LINEAR PROGRAMMING;

EID: 85162041468     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (9)

1. Burnetas, A.N. & Katehakis, M.N. (1997) Optimal adaptive policies for Markov decision processes. Mathematics of Operations Research 22(1):222-255 (Pubitemid 127621321)
2. Auer, P. & Ortner, R. (2007) Logarithmic online regret bounds for undiscounted reinforcement learning. Advances in Neural Information Processing Systems 19. Cambridge, MA: MIT Press.
3. Lai, T.L. & Robbins, H. (1985) Asymptotically efficient adaptive allocation rules. Advances in Applied Mathematics 6(1):4-22.
4. Brafman, R.I. & Tennenholtz, M. (2002) R-MAX - a general polynomial time algorithm for near-optimal reinforcement learning. Journal of Machine Learning Research 3:213-231.
5. Auer, P. (2002) Using confidence bounds for exploitation-exploration trade-offs. Journal of Machine Learning Research 3:397-422.
6. Auer, P., Cesa-Bianchi, N. & and Fischer, P. (2002) Finite-time analysis of the multiarmed bandit problem. Machine Learning 47(2-3):235-256. (Pubitemid 34126111)
7. Strehl, A.L. & Littman, M. (2005) A theoretical analysis of model-based interval estimation. In Proceedings of the Twenty-Second International Conference on Machine Learning, pp. 857-864. ACM Press. (Pubitemid 43183415)
8. Tewari, A. (2007) Reinforcement Learning in Large or Unknown MDPs. PhD thesis, Department of Electrical Engineering and Computer Sciences, University of California at Berkeley.
9. Puterman, M.L. (1994) Markov Decision Processes: Discrete Stochastic Dynamic Programming. New York: John Wiley and Sons.