Near-optimal regret bounds for reinforcement learning

Advances in Neural Information Processing Systems 21 - Proceedings of the 2008 Conference

Volumn , Issue , 2009, Pages 89-96

  Auer, Peter ^a   Jaksch, Thomas ^a   Ortner, Ronald ^a  

^a UNIVERSITY OF LEOBEN   (Austria)

Author keywords

[No Author keywords available]

Indexed keywords

LEARNING ALGORITHMS; REINFORCEMENT LEARNING;

MARKOV DECISION PROCESSES; NEAR-OPTIMAL; NEW PARAMETERS; OPTIMAL POLICIES; OPTIMAL REGRET; REGRET BOUNDS; REINFORCEMENT LEARNING ALGORITHMS; REINFORCEMENT LEARNINGS; S STATE; TRANSITION STRUCTURES;

MARKOV PROCESSES;

EID: 73549103329     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (13)

1. Richard S. Sutton and Andrew G. Barto. Reinforcement Learning: An Introduction. MIT Press, 1998.
2. Michael J. Kearns and Satinder P. Singh. Finite-sample convergence rates for Q-learning and indirect algorithms. In Advances in Neural Information Processing Systems 11. MIT Press, 1999.
3. Michael J. Kearns and Satinder P. Singh. Near-optimal reinforcement learning in polynomial time. Mach. Learn., 49:209-232, 2002.
4. Martin L. Puterman. Markov Decision Processes: Discrete Stochastic Dynamic Programming. John Wiley & Sons, Inc., New York, NY, USA, 1994.
5. Peter Auer and Ronald Ortner. Logarithmic online regret bounds for reinforcement learning. In Advances in Neural Information Processing Systems 19, pages 49-56. MIT Press, 2007.
6. 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.
7. Ambuj Tewari and Peter Bartlett. Optimistic linear programming gives logarithmic regret for irreducible mdps. In Advances in Neural Information Processing Systems 20, pages 1505-1512. MIT Press, 2008.
8. Sham M. Kakade. On the Sample Complexity of Reinforcement Learning. PhD thesis, University College London, 2003.
9. Alexander L. Strehl and Michael L. Littman. A theoretical analysis of model-based interval estimation. In Proc. 22nd ICML 2005, pages 857-864, 2005. (Pubitemid 43183415)
10. Alexander L. Strehl and Michael L. Littman. An analysis of model-based interval estimation for Markov decision processes. J. Comput. System Sci., 74(8):1309-1331, 2008.
11. Apostolos N. Burnetas and Michael N. Katehakis. Optimal adaptive policies for Markov decision processes. Math. Oper. Res., 22(1):222-255, 1997. (Pubitemid 127621321)
12. Eyal Even-Dar, Sham M. Kakade, and Yishay Mansour. Experts in a Markov decision process. In Advances in Neural Information Processing Systems 17, pages 401-408. MIT Press, 2005.
13. Peter Auer, Thomas Jaksch, and Ronald Ortner. Near-optimal regret bounds for reinforcement learning. Technical Report CIT-2009-01, University of Leoben, Chair for Information Technology, 2009. http://institute.unileoben.ac.at/ infotech/publications/TR/CIT-2009-01.pdf.