| 来源类型 | Book Section |
| DOI | 10.5220/0007349000800091 |
| Reinforcement Learning Approach for Cooperative Control of Multi-Agent Systems. | |
| Cayuela VP; Seix BM; Orduña-Cabrera F | |
| 发表日期 | 2019 |
| 出处 | 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 DOI: 10.5220/0007349000800091 . |
| 出版年 | 2019 |
| 语种 | 英语 |
| 摘要 | 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. |
| 主题 | Advanced Systems Analysis (ASA) ; Ecosystems Services and Management (ESM) |
| 关键词 | Distributed Control, Intelligent Agents, Reinforcement Learning, Cooperative Agents |
| URL | http://pure.iiasa.ac.at/id/eprint/15790/ |
| 来源智库 | International Institute for Applied Systems Analysis (Austria) |
| 引用统计 | |
| 资源类型 | 智库出版物 |
| 条目标识符 | http://119.78.100.153/handle/2XGU8XDN/134682 |
| 推荐引用方式 GB/T 7714 | Cayuela VP,Seix BM,Orduña-Cabrera F. Reinforcement Learning Approach for Cooperative Control of Multi-Agent Systems.. 2019. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
| ICAART_2019_55_CR.pd（640KB） | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 | ||
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