Optimal tuning of continual online exploration in reinforcement learning

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4131 LNCS - I, Issue , 2006, Pages 790-800

  Achbany, Youssef ^a   Fouss, Francois ^a   Yen, Luh ^a   Pirotte, Alain ^a   Saerens, Marco ^a  

^a UNIVERSITÉ CATHOLIQUE DE LOUVAIN   (Belgium)

Author keywords

[No Author keywords available]

Indexed keywords

ALGORITHMS; ENTROPY; ONLINE SYSTEMS; OPTIMAL SYSTEMS; OPTIMIZATION; PROBABILITY DISTRIBUTIONS;

BELLMAN EQUATIONS; CONTINUAL ONLINE EXPLORATION; OPTIMAL TUNING;

LEARNING SYSTEMS;

EID: 33749864692     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11840817_82     Document Type: Conference Paper

References (24)

1. Y. Achbany, F. Fouss, L. Yen, A. Pirotte, and M. Saerens, Timing continual exploration in reinforcement learning. Technical report, http://www.isys.ucl.ac.be/staff/francois/Articles/Achbany2005a.pdf, 2005.
2. M. S. Bazaraa, H. D. Sherali, and C. M. Shetty, Nonlinear programming: Theory and algorithms. John Wiley and Sons, 1993.
3. D. P. Bertsekas. N euro-dynamic programming. Athena Scientific, 1996.
4. D. P. Bertsekas. Network optimization: continuous and discrete models. Athena Scientific, 1998.
5. D. P. Bertsekas. Dynamic programming and optimal control. Athena sientific, 2000.
6. J. A. Boyan and M. L. Littman. Packet routing in dynamically changing networks: A reinforcement learning approach. Advances in Neural Information Processing Systems 6 (NIPS6), pages 671-678, 1994.
7. R. G. Brown. Smoothing, forecasting and prediction of discrete time series. Prentice-hall, 1962.
8. N. Christofides. Graph theory: An algorithmic approach. Academic Press, 1975.
9. T. M. Cover and J. A. Thomas. Elements of information theory. John Wiley and Sons, 1991.
10. J. N. Kapur and H. K. Kesavan. Entropy optimization principles with applications. Academic Press, 1992.
11. J. G. Kemeny and J. L. Snell. Finite markov chains. Springer-Verlag, 1976.
12. M. J. Osborne. An introduction to game theory. Oxford University Press, 2004.
13. H. Raiffa. Decision analysis. Aclclison-Wesley, 1970.
14. G. Rummery and M. Niranjan. On-line q-learning using connectionist systems. Technical Report CUED/F-INFENC/TR 166, Cambridge University Engineering Departement, 1994.
15. G. Shani, R. Brafman, and S. Shimony. Adaptation for changing stochastic environments through online pomdp policy learning. In Workshop on Reinforcement Learning in Non-Stationary Environments, ECML 2005, pages 61-70, 2005.
16. S. Singh and R. Sutton. Reinforcement learning with replacing eligibility traces. Machine Learning, 22:123-158, 1996.
17. J. C. Spall. Introduction to stochastic search and optimization. Wiley, 2003.
18. R. S. Sutton and A. G. Barto. Reinforcement learning: an introduction. The MIT Press, 1998.
19. S. Thrun. Efficient exploration in reinforcement learning. Technical report, School of Computer Science, Carnegie Mellon University, 1992.
20. S. Thrun. The role of exploration in learning control. In D. White and D. Sofge, editors, Handbook for Intelligent Control: Neural, Fuzzy and Adaptive Approaches. Van Nostrand Reinhold, Florence, Kentucky 41022, 1992.
21. S. Thrun, W. Burgard, and D. Fox. Probabilistic Robotics. MIT Press, 2005.
22. K. Verbeeck. Coordinated exploration in multi-agent reinforcement learning. PhD thesis, Vrije Universiteit Brussel, Belgium, 2004.
23. J. C. Watkins. Learning from delayed rewards. PhD thesis, King's College of Cambridge, UK, 1989.
24. J. C. Watkins and P. Dayan. Q-learning. Machine Learning, 8(3-4):279-292, 1992.