Learning with whom to communicate using relational reinforcement learning

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Learning with whom to communicate using relational reinforcement learning

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International Conference on Autonomous Agents archive
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 2 table of contents

Budapest, Hungary

SESSION: Interactions table of contents

Pages 1221-1222

Year of Publication: 2009

ISBN:978-0-9817381-7-8

Authors

Marc Ponsen	University of Maastricht, The Netherlands
Tom Croonenborghs	KH Kempen, Belgium
Karl Tuyls	Eindhoven University of Technology, The Netherlands
Jan Ramon	K.U. Leuven, Belgium
Kurt Driessens	K.U. Leuven, Belgium

Sponsors

: The Foundation for Intelligent Physical Agents
Microsoft Research : Microsoft Research
: Whitestein Technologies
: European Office of Aerospace Research and Development, Air Force Office of Scientific Research, United States Air Force Research Laboratory
: Drexel University
: Wiley -- Blackwell Ltd

Publisher

International Foundation for Autonomous Agents and Multiagent Systems Richland, SC

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ABSTRACT

Relational reinforcement learning (RRL) has emerged in the machine learning community as a new promising subfield of reinforcement learning (RL) (e.g. [1]). It upgrades RL techniques by using relational representations for states, actions and learned value-functions or policies to allow more natural representations and abstractions of complex tasks. This leads to a serious state space reduction, allowing to better generalize and infer new knowledge.

REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

	1	K. Driessens. Relational Reinforcement Learning. PhD thesis, Department of Computer Science, Katholieke Universiteit Leuven, 2004.
	2	Kurt Driessens , Jan Ramon , Hendrik Blockeel, Speeding Up Relational Reinforcement Learning through the Use of an Incremental First Order Decision Tree Learner, Proceedings of the 12th European Conference on Machine Learning, p.97-108, September 05-07, 2001