Abstract
![CDATA[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.]]
Original language | English |
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Title of host publication | Proceedings of the 19th European Conference on Artificial Intelligence (ECAI 2010) : 16-20 August 2010, Lisbon, Portugal |
Publisher | IOS Press BV |
Pages | 355-360 |
Number of pages | 6 |
ISBN (Print) | 9781607506058 |
Publication status | Published - 2010 |
Event | European Conference on Artificial Intelligence - Duration: 27 Aug 2012 → … |
Conference
Conference | European Conference on Artificial Intelligence |
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Period | 27/08/12 → … |