Case-based multiagent reinforcement learning : cases as heuristics for selection of actions

Ramón López de Mántaras, Reinaldo A. C. Bianchi

    Research output: Chapter in Book / Conference PaperConference Paperpeer-review

    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 languageEnglish
    Title of host publicationProceedings of the 19th European Conference on Artificial Intelligence (ECAI 2010) : 16-20 August 2010, Lisbon, Portugal
    PublisherIOS Press BV
    Pages355-360
    Number of pages6
    ISBN (Print)9781607506058
    Publication statusPublished - 2010
    EventEuropean Conference on Artificial Intelligence -
    Duration: 27 Aug 2012 → …

    Conference

    ConferenceEuropean Conference on Artificial Intelligence
    Period27/08/12 → …

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