Case-based multiagent reinforcement learning: Cases as heuristics for selection of actions
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2010-08-17
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Reinaldo Bianchi
LOPEZ DE MANTARAS, R.
LOPEZ DE MANTARAS, R.
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Frontiers in Artificial Intelligence and Applications
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BIANCHI, R.; LOPEZ DE MANTARAS, R. Case-based multiagent reinforcement learning: Cases as heuristics for selection of actions. Frontiers in Artificial Intelligence and Applications, v.215, p. 35-360, 2010.
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This work presents a new approach that allows the use of cases in a case base as heuristics to speed up Multiagent Reinforcement Learning algorithms, combining Case-Based Reasoning (CBR) and Multiagent Reinforcement Learning (MRL) techniques. This approach, called Case-Based Heuristically Accelerated Multiagent Reinforcement Learning (CB-HAMRL), builds upon an emerging technique, Heuristic Accelerated Reinforcement Learning (HARL), in which RL methods are accelerated by making use of heuristic information. CB-HAMRL is a subset of MRL that makes use of a heuristic function ℋ derived from a case base, in a Case-Based Reasoning manner. An algorithm that incorporates CBR techniques into the Heuristically Accelerated Minimax-Q is also proposed and a set of empirical evaluations were conducted in a simulator for the Littman's robot soccer domain, comparing the three solutions for this problem: MRL, HAMRL and CB-HAMRL. Experimental results show that using CB-HAMRL, the agents learn faster than using RL or HAMRL methods. © 2010 The authors and IOS Press. All rights reserved.