Kagan Tumer's Publications

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Time-Extended Policies in Multiagent Reinforcement Learning. K. Tumer and A. Agogino. In Proceedings of the Third International Joint Conference on Autonomous Agents and Multiagent Systems, pp. 1336–1337, New York, NY, July 2004.

Abstract

Many algorithms such as Q-learning successfully address reinforcement learning in single-agent multi-time-step problems. In addition there are methods that address reinforcement learning in multi-agent single-time-step problems. However, unmodified single-agent multi-time-step methods and multi-agent single-time-step methods cannot necessarily be combined to solve multi-agent multi-time-step problems due to strong coupling between multi-agent interactions between time steps. Rewards that result in multi-agent collaboration for a single time-step may result in poor collaboration in future time-steps. This paper shows how to avoid this problem.

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BibTeX Entry

@inproceedings{tumer-agogino_marl_aamas04,
	author = {K. Tumer and A. Agogino},
	title = {Time-Extended Policies in Multiagent Reinforcement Learning},
	booktitle = {Proceedings of the Third International Joint Conference on
		Autonomous Agents and Multiagent Systems},
	pages = {1336-1337},
	month = {July},
	address = {New York, NY},
	abstract = {Many algorithms such as Q-learning successfully address reinforcement learning in single-agent multi-time-step problems. In addition there are methods that address reinforcement learning in multi-agent single-time-step problems. However, unmodified single-agent multi-time-step methods and multi-agent single-time-step methods cannot necessarily be combined to solve multi-agent multi-time-step problems due to strong coupling between multi-agent interactions between time steps. Rewards that result in multi-agent collaboration for a single time-step may result in poor collaboration in future time-steps. This paper shows how to avoid this problem.},
	bib2html_pubtype = {Refereed Conference Papers},
	bib2html_rescat = {Multiagent Systems, Reinforcement Learning},
	year = {2004}
}

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