A multi-agent cooperative reinforcement learning model using a hierarchy of consultants, tutors and workers

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

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