Logarithmic Online Regret Bounds for Undiscounted Reinforcement Learning

NIPS 2006: Proceedings of the 19th International Conference on Neural Information Processing Systems

Volumn , Issue , 2006, Pages 49-56

  Auer, Peter ^a   Ortner, Ronald ^a  

^a UNIVERSITY OF LEOBEN   (Austria)

Author keywords

[No Author keywords available]

Indexed keywords

E-LEARNING; LEARNING ALGORITHMS;

EXPLORATION/EXPLOITATION; FINITE NUMBER; MULTIARMED BANDIT PROBLEMS (MABP); ON-LINE PERFORMANCE; OPTIMAL POLICIES; REGRET BOUNDS; REINFORCEMENT LEARNINGS; UPPER CONFIDENCE BOUND;

REINFORCEMENT LEARNING;

EID: 85151789426     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (16)

1. Michael J. Kearns and Satinder P. Singh. Near-optimal reinforcement learning in polynomial time. Mach. Learn., 49:209-232, 2002.
2. Ronen I. Brafman and Moshe Tennenholtz. R-max - a general polynomial time algorithm for near-optimal reinforcement learning. J. Mach. Learn. Res., 3:213-231, 2002.
3. Sham M. Kakade. On the Sample Complexity of Reinforcement Learning. PhD thesis, University College London, 2003.
4. Alexander L. Strehl and Michael L. Littman. A theoretical analysis of model-based interval estimation. In Proc. 22nd ICML 2005, pages 857-864, 2005.
5. Alexander L. Strehl, Lihong Li, Eric Wiewiora, John Langford, and Michael L. Littman. Pac model-free reinforcement learning. In Proc. 23nd ICML 2006, pages 881-888, 2006.
6. Claude-Nicolas Fiechter. Efficient reinforcement learning. In Proc. 7th COLT, pages 88-97. ACM, 1994.
7. Peter Auer and Ronald Ortner. Online regret bounds for a new reinforcement learning algorithm. In Proc. 1st ACVW, pages 35-42. ÖCG, 2005.
8. Peter Auer. Using confidence bounds for exploitation-exploration trade-offs. J. Mach. Learn. Res., 3:397-422, 2002.
9. Peter Auer, Nicolò Cesa-Bianchi, and Paul Fischer. Finite-time analysis of the multi-armed bandit problem. Mach. Learn., 47:235-256, 2002.
10. Alexander L. Strehl and Michael L. Littman. An empirical evaluation of interval estimation for Markov decision processes. In Proc. 16th ICTAI, pages 128-135. IEEE Computer Society, 2004.
11. Leslie P. Kaelbling. Learning in Embedded Systems. MIT Press, 1993.
12. Eyal Even-Dar, Shie Mannor, and Yishay Mansour. Action elimination and stopping conditions for reinforcement learning. In Proc. 20th ICML, pages 162-169. AAAI Press, 2003.
13. Apostolos N. Burnetas and Michael N. Katehakis. Optimal adaptive policies for Markov decision processes. Math. Oper. Res., 22(1):222-255, 1997.
14. Eyal Even-Dar, Sham M. Kakade, and Yishay Mansour. Experts in a Markov decision process. In Proc. 17th NIPS, pages 401-408. MIT Press, 2004.
15. Martin L. Puterman. Markov Decision Processes. Discrete Stochastic Programming. Wiley, 1994.
16. Grace E. Cho and Carl D. Meyer. Markov chain sensitivity measured by mean first passage times. Linear Algebra Appl., 316:21-28, 2000.