Combining exploitation-based and exploration-based approach in reinforcement learning

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

Volumn 1983, Issue , 2000, Pages 326-331

  Iwata, Kazunori ^a   Ito, Nobuhiro ^a   Yamauchi, Koichiro ^a   Ishii, Naohiro ^a  

^a NAGOYA INSTITUTE OF TECHNOLOGY   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

DATA MINING; ENGINEERING EDUCATION; INTELLIGENT AGENTS; MACHINE LEARNING;

EXPLOITATION AND EXPLORATIONS; EXPLORATION STRATEGIES; GLOBAL STATE; MODEL BASED APPROACH; MODEL-FREE METHOD; MODELING-BASED LEARNING; OPTIMAL POLICIES; TRIAL AND ERROR;

REINFORCEMENT LEARNING;

EID: 73849149073     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/3-540-44491-2_47     Document Type: Conference Paper

References (8)

1. Tom Mitchell, Machine Learning, McGraw Hill, p367-390, 1997.
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3. Watkins, C.J.C.H., Dayan, P. Technical Note : Q-learning, Machine Learning 8, p55-68, 1992.
4. Leslie Pack Kaelbling, Michael L. Littman, Andrew W. Moore, Reinforcement Learning : A Survey, Journal of Artificial Intelligence Research 4, p237-285, 1996.
5. Sutton R., Barto A., Reinforcement Learning : An Introduction, MIT Press, 1998.
6. S. D. Whitehead and D. H. Ballard, Learning to perceive and act by trial and error, Machine Learning, 7, p45-83, 1991.
7. Feller, W. An Introduction to Probability Theory and Its Applications, Modern Asia Editions, 1960.
8. Richard S. Sutton, Temporal Credit Assignment in Reinforcement Learning, PhD thesis, University of Massachusetts, Amherst, Massachusetts, 1984.