Abstract
It is possible to exploit automated learning from sensed data for practical applications - in essence facilitating reasoning about particular problem domains based on a combination of environmental awareness and insights elicited from past decisions. We explore some enhanced Reinforcement Learning (RL) methods used for achieving such machine learning using software agents in order to address two questions. Can RL implementations/methods be accelerated by using a Multi-Agent approach? Can an agent learn composite skills in single-pass?
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References
Puterman,M.L., Markov Decision Processes: Discrete Stochastic Dynamic Programming. 2005: Wiley-Interscience
Sutton,R. & Barto,A., Reinforcement Learning: An Introduction. 1998: MIT Press
Watkins,C. & Dayan,P., Q-Learning. Machine Learning, 8(3-4):279–292, 1992, 1992
Bell, Luo & McCollum, Skill Combination in Reinforcement Learning, 2008
Tesauro,G. Temporal Difference Learning & TD-Gammon, 1995
Agogino,A. & Tumer,K, Quicker Q-Learning in Multi-Agent Systems, 2008
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© 2010 Springer-Verlag London
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Graham, C., Bell, D., Luo, Z. (2010). Multi-Agent Reinforcement Learning – An Exploration Using Q-Learning. In: Bramer, M., Ellis, R., Petridis, M. (eds) Research and Development in Intelligent Systems XXVI. Springer, London. https://doi.org/10.1007/978-1-84882-983-1_21
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DOI: https://doi.org/10.1007/978-1-84882-983-1_21
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