Reinforcement Learning Approach for Cooperative Control of Multi-Agent Systems

Javalera Rincón, V. ORCID: https://orcid.org/0000-0001-8743-9777, Cayuela, V.P., Seix, B.M., & Orduña-Cabrera, F. ORCID: https://orcid.org/0000-0002-8558-0053 (2019). Reinforcement Learning Approach for Cooperative Control of Multi-Agent Systems. In: Proceedings of the 11th International Conference on Agents and Artificial Intelligence (ICAART 2019). pp. 80-91 Porto, Portugal: ICAART. ISBN 978-989-758-350-6 10.5220/0007349000800091.

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Abstract

Reinforcement Learning (RL) systems are trial-and-error learners. This feature altogether with delayed reward, makes RL flexible, powerful and widely accepted. However, RL could not be suitable for control of critical systems where the learning of the control actions by trial and error is not an option. In the RL literature, the use of simulated experience generated by a model is called planning. In this paper, the planningByInstruction and planningByExploration techniques are introduced, implemented and compared to coordinate, a heterogeneous multi-agent architecture for distributed Large Scale Systems (LSS). This architecture was proposed by (Javalera 2016). The models used in this approach are part of a distributed architecture of agents. These models are used to simulate the behavior of the system when some coordinated actions are applied. This experience is learned by the so-called, LINKER agents, during an off-line training. An exploitation algorithm is used online, to coordinate and optimize the value of overlapping control variables of the agents in the distributed architecture in a cooperative way. This paper also presents a technique that offers a solution to the problem of the number of learning steps required to converge toward an optimal (or can be sub-optimal) policy for distributed control systems. An example is used to illustrate the proposed approach, showing exciting and promising results regarding the applicability to real systems.

Item Type:	Book Section
Uncontrolled Keywords:	Distributed Control, Intelligent Agents, Reinforcement Learning, Cooperative Agents
Research Programs:	Advanced Systems Analysis (ASA) Ecosystems Services and Management (ESM)
Depositing User:	Luke Kirwan
Date Deposited:	12 Mar 2019 07:31
Last Modified:	01 Feb 2023 05:00
URI:	https://pure.iiasa.ac.at/15790

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