Kagan Tumer's Publications

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CLEAN Rewards to Improve Coordination by Removing Exploratory Action Noise. C. HolmesParker, M. Taylor, A. Agogino, and K. Tumer. In International Conference on Intelligent Agent Technology, Warsaw, Poland, August 2014.

Abstract

Coordinating the joint-actions of agents in coop- erative multiagent systems is a difficult problem in many real world domains. Learning in such multiagent systems can be slow because an agent may not only need to learn how to behave in a complex environment, but also to account for the actions of other learning agents. The inability of an agent to distinguish between the true environmental dynamics and those caused by the stochastic exploratory actions of other agents creates noise in each agentŐs reward signal. This learning noise can have unforeseen and often undesirable effects on the resultant system performance. We define such noise as exploratory action noise, demonstrate the critical impact it can have on the learning process in multiagent settings, and introduce a reward structure to effectively remove such noise from each agentŐs reward signal. In particular, we introduce two types of Coordinated Learning without Exploratory Action Noise (CLEAN) rewards that allow an agent to estimate the counterfactual reward it would have received had it taken an alternative action. We empirically show that CLEAN rewards outperform agents using both traditional global rewards and shaped difference rewards in two domains.

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BibTeX Entry

@inproceedings{tumer-holmesparker_iat14,
        author = {C. HolmesParker and M. Taylor and A. Agogino and  K. Tumer},
        title = {CLEAN Rewards to Improve Coordination by Removing Exploratory Action Noise},
        booktitle = {International Conference on Intelligent Agent Technology},
	month = {August},
	address = {Warsaw, Poland},
        abstract={Coordinating the joint-actions of agents in coop- erative multiagent systems is a difficult problem in many real world domains. Learning in such multiagent systems can be slow because an agent may not only need to learn how to behave in a complex environment, but also to account for the actions of other learning agents. The inability of an agent to distinguish between the true environmental dynamics and those caused by the stochastic exploratory actions of other agents creates noise in each agentÕs reward signal. This learning noise can have unforeseen and often undesirable effects on the resultant system performance. We define such noise as exploratory action noise, demonstrate the critical impact it can have on the learning process in multiagent settings, and introduce a reward structure to effectively remove such noise from each agentÕs reward signal. In particular, we introduce two types of Coordinated Learning without Exploratory Action Noise (CLEAN) rewards that allow an agent to estimate the counterfactual reward it would have received had it taken an alternative action. We empirically show that CLEAN rewards outperform agents using both traditional global rewards and shaped difference rewards in two domains.},
        	bib2html_pubtype = {Refereed Conference Papers},
	bib2html_rescat = {Multiagent Systems, Reinforcement Learning},
        year = {2014}
        }  

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