- Title
- A multi-agent cooperative reinforcement learning model using a hierarchy of consultants, tutors and workers
- Creator
- Abed-Alguni, Bilal H.; Chalup, Stephan K.; Henskens, Frans A.; Paul, David J.
- Relation
- Vietnam Journal of Computer Science Vol. 2, Issue 4, p. 213-226
- Publisher Link
- http://dx.doi.org/10.1007/s40595-015-0045-x
- Publisher
- SpringerOpen
- Resource Type
- journal article
- Date
- 2015
- Description
- 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.
- Subject
- reinforcement learning; Q-learning; multi-agent system; distributed system; Markov decision process; factored Markov decision process
- Identifier
- http://hdl.handle.net/1959.13/1321576
- Identifier
- uon:24397
- Identifier
- ISSN:2196-8888
- Rights
- © The Author(s) 2015. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
- Language
- eng
- Full Text
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