Adaptive ε-greedy exploration in reinforcement learning based on value differences

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

Volumn 6359 LNAI, Issue , 2010, Pages 203-210

  Tokic, Michel ^a,b  

^a UNIVERSITY OF APPLIED SCIENCES RAVENSBURG WEINGARTEN   (Germany)

^b UNIVERSITY OF ULM   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

AD HOC APPROACH; COMMONLY USED; EXPLORATION/EXPLOITATION DILEMMAS; GREEDY EXPLORATION; MULTI ARMED BANDIT; TEMPORAL DIFFERENCE ERRORS; VALUE FUNCTIONS;

ARTIFICIAL INTELLIGENCE; REINFORCEMENT LEARNING;

POTASSIUM IODIDE; REINFORCEMENT LEARNING;

EID: 78349245906     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-16111-7_23     Document Type: Conference Paper

References (14)

1. Sutton, R.S., Barto, A.G.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)
2. Watkins, C.: Learning from Delayed Rewards. PhD thesis, University of Cambridge, Cambridge, England (1989)
3. Thrun, S.B.: Efficient exploration in reinforcement learning. Technical Report CMU-CS-92-102, Carnegie Mellon University, Pittsburgh, PA, USA (1992)
4. Brafman, R.I., Tennenholtz, M.: R-MAX - a general polynomial time algorithm for near-optimal reinforcement learning. Journal of Machine Learning Research 3, 213-231 (2002)
5. Ishii, S., Yoshida, W., Yoshimoto, J.: Control of exploitation- exploration metaparameter in reinforcement learning. Neural Networks 15(4-6), 665-687 (2002)
6. Heidrich-Meisner, V.: Interview with Richard S. Sutton. Künstliche Intelligenz 3, 41-43 (2009)
7. Vermorel, J., Mohri, M.: Multi-armed bandit algorithms and empirical evaluation. In: Gama, J., Camacho, R., Brazdil, P.B., Jorge, A.M., Torgo, L. (eds.) ECML 2005. LNCS (LNAI), vol. 3720, pp. 437-448. Springer, Heidelberg (2005)
8. Caelen, O., Bontempi, G.: Improving the exploration strategy in bandit algorithms. In: Maniezzo, V., Battiti, R., Watson, J.-P. (eds.) LION 2007 II. LNCS, vol. 5313, pp. 56-68. Springer, Heidelberg (2008)
9. Rummery, G.A., Niranjan, M.: On-line Q-learning using connectionist systems. Technical Report CUED/F-INFENG/TR 166, Cambridge University (1994)
10. Bertsekas, D.P.: Dynamic Programming: Deterministic and Stochastic Models. Prentice-Hall, Englewood Cliffs (1987)
11. George, A.P., Powell, W.B.: Adaptive stepsizes for recursive estimation with applications in approximate dynamic programming. Machine Learning 65(1), 167-198 (2006)
12. Robbins, H.: Some aspects of the sequential design of experiments. Bulletin of the American Mathematical Society 58, 527-535 (1952)
13. Awerbuch, B., Kleinberg, R.D.: Adaptive routing with end-to-end feedback: Distributed learning and geometric approaches. In: Proceedings of the 36th Annual ACM Symposium on Theory of Computing, Chicago, IL, USA, pp. 45-53. ACM, New York (2004)
14. Azoulay-Schwartz, R., Kraus, S., Wilkenfeld, J.: Exploitation vs. exploration: Choosing a supplier in an environment of incomplete information. Decision Support Systems 38(1), 1-18 (2004)