Please use this identifier to cite or link to this item:
https://hdl.handle.net/1959.11/17940
Title: | A multi-agent cooperative reinforcement learning model using a hierarchy of consultants, tutors and workers | Contributor(s): | Abed-alguni, Bilal H (author); Chalup, Stephan K (author); Henskens, Frans A (author); Paul, David (author) | Publication Date: | 2015 | Open Access: | Yes | DOI: | 10.1007/s40595-015-0045-x | Handle Link: | https://hdl.handle.net/1959.11/17940 | Abstract: | The hierarchical organisation of distributed systems can provide an efficient decomposition for machine learning. This paper proposes an algorithm for cooperative policy construction for independent learners, named Q-learning with aggregation (QA-learning). The algorithm is based on a distributed hierarchical learning model and utilises three specialisations of agents: workers, tutors and consultants. The consultant agent incorporates the entire system in its problem space, which it decomposes into sub-problems that are assigned to the tutor and worker agents. The QA-learning algorithm aggregates the Q-tables of worker agents into a central repository managed by their tutor agent. Each tutor's Q-table is then incorporated into the consultant's Q-table, resulting in a Q-table for the entire problem. The algorithm was tested using a distributed hunter prey problem, and experimental results show that QA-learning converges to a solution faster than single agent Q-learning and some famous cooperative Q-learning algorithms. | Publication Type: | Journal Article | Source of Publication: | Vietnam Journal of Computer Science, 2(4), p. 213-226 | Publisher: | SpringerOpen | Place of Publication: | Germany | ISSN: | 2196-8896 2196-8888 |
Fields of Research (FoR) 2008: | 080501 Distributed and Grid Systems 080101 Adaptive Agents and Intelligent Robotics |
Fields of Research (FoR) 2020: | 460605 Distributed systems and algorithms 460604 Dependable systems |
Socio-Economic Objective (SEO) 2008: | 970108 Expanding Knowledge in the Information and Computing Sciences | Socio-Economic Objective (SEO) 2020: | 280115 Expanding knowledge in the information and computing sciences | Peer Reviewed: | Yes | HERDC Category Description: | C1 Refereed Article in a Scholarly Journal |
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Appears in Collections: | Journal Article |
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