Publication Type

Conference Proceeding Article

Version

acceptedVersion

Publication Date

8-2014

Abstract

This work addresses the coordination issue in distributed optimization problem (DOP) where multiple distinct and time-critical tasks are performed to satisfy a global objective function. The performance of these tasks has to be coordinated due to the sharing of consumable resources and the dependency on non-consumable resources. Knowing that it can be sub-optimal to predefine the performance of the tasks for large DOPs, the multi-agent reinforcement learning (MARL) framework is adopted wherein an agent is used to learn the performance of each distinct task using reinforcement learning. To coordinate MARL, we propose a novel coordination strategy integrating Motivated Learning (ML) and the k-Winner-Take-All (k-WTA) approach. The priority of the agents to the shared resources is determined using Motivated Learning in real time. Due to the finite amount of the shared resources, the k-WTA approach is used to allow for the maximum number of the most urgent tasks to execute. Agents performing tasks dependent on resources produced by other agents are coordinated using domain knowledge. Comparing our proposed contribution to the existing approaches, results from our experiments based on a 16-task DOP and a 68-task DOP show our proposed approach to be most effective in coordinating multi-agent reinforcement learning.

Keywords

Reinforcement learning, Multi-agent reinforcement learning

Discipline

Artificial Intelligence and Robotics | Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT): Warsaw, August 11-14

First Page

190

Last Page

197

ISBN

9781479941438

Identifier

10.1109/IJCNN.2014.6889624

Publisher

IEEE Computer Society

City or Country

Los Alamitos, CA

Additional URL

https://doi.org/10.1109/WI-IAT.2012.154

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