Author(s) |
Abed-alguni, Bilal H
Chalup, Stephan K
Henskens, Frans A
Paul, David
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Publication Date |
2015
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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.
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Citation |
Vietnam Journal of Computer Science, 2(4), p. 213-226
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ISSN |
2196-8896
2196-8888
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Link | |
Publisher |
SpringerOpen
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Title |
A multi-agent cooperative reinforcement learning model using a hierarchy of consultants, tutors and workers
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Type of document |
Journal Article
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Entity Type |
Publication
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