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» Q-Decomposition for Reinforcement Learning Agents
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IJCAI
2007
13 years 9 months ago
Utile Distinctions for Relational Reinforcement Learning
We introduce an approach to autonomously creating state space abstractions for an online reinforcement learning agent using a relational representation. Our approach uses a tree-b...
William Dabney, Amy McGovern
IJCAI
2001
13 years 9 months ago
Rational and Convergent Learning in Stochastic Games
This paper investigates the problem of policy learning in multiagent environments using the stochastic game framework, which we briefly overview. We introduce two properties as de...
Michael H. Bowling, Manuela M. Veloso
GECON
2008
Springer
134views Business» more  GECON 2008»
13 years 8 months ago
Rational Bidding Using Reinforcement Learning
The application of autonomous agents by the provisioning and usage of computational resources is an attractive research field. Various methods and technologies in the area of arti...
Nikolay Borissov, Arun Anandasivam, Niklas Wirstr&...
AE
2003
Springer
14 years 28 days ago
An Agent Model for First Price and Second Price Private Value Auctions
The aim of this research is to develop an adaptive agent based model of auction scenarios commonly used in auction theory to help understand how competitors in auctions reach equil...
Anthony J. Bagnall, Iain Toft
ROBOCUP
2007
Springer
167views Robotics» more  ROBOCUP 2007»
14 years 1 months ago
Cooperative/Competitive Behavior Acquisition Based on State Value Estimation of Others
The existing reinforcement learning approaches have been suffering from the curse of dimension problem when they are applied to multiagent dynamic environments. One of the typical...
Kentarou Noma, Yasutake Takahashi, Minoru Asada