Abed-alguni, Bilal H

Chalup, Stephan K

Henskens, Frans A

Paul, David

2015

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.

Vietnam Journal of Computer Science, 2(4), p. 213-226

2196-8896

2196-8888

https://hdl.handle.net/1959.11/17940

en

SpringerOpen

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

Journal Article

Publication