Controlling Multiple Cranes Using Multi-Agent Reinforcement Learning: Emerging Coordination Among Competitive Agents

Sachiyo Arai; Kazuteru Miyazaki; Shigenobu Kobayashi

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Controlling Multiple Cranes Using Multi-Agent Reinforcement Learning: Emerging Coordination Among Competitive Agents

机译：使用多智能体强化学习控制多台起重机：竞争主体之间的新兴协调

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This paper describes the Profit-Sharing, a re- inforcement learning approach which can be used to design a coordination strategy in a multi-agent system, and demonstrates its effectiveness empirically within a coil-yard of steel manufac- ture. This domain consists of multiple cranes which are oper- ated asynchronously but need coordination to adjust their initial plans of task execution to avoid the collisions, which would be caused by resource limitation.

机译：本文介绍了利润分享（一种强化学习方法，可用于在多主体系统中设计协调策略），并在钢卷生产场内以经验证明了其有效性。该领域由多个起重机组成，这些起重机异步运行，但需要协调以调整其初始任务执行计划，以避免由资源限制引起的冲突。

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来源
《IEICE Transactions on Communications》 |2000年第5期|p.1039-1047|共9页
作者
Sachiyo Arai; Kazuteru Miyazaki; Shigenobu Kobayashi;
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原文格式 PDF
正文语种 eng
中图分类无线电电子学、电信技术;
关键词
reinforcement learning; multi-agent system; profit-sharing;

机译：强化学习;多代理系统;利润分享;

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1. A multi-agent reinforcement learning method with learning of other agents for competitive game [J] . Yoichiro Matsuno, Tatsuya Yamazaki, Jun Matsuda, 電子情報通信学会技術研究報告. ニュ-ロコンピュ-ティング. Neurocomputing . 2000,第688期

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2. A multi-agent reinforcement learning method with learning of other agents for competitive game [J] . Yoichiro Matsuno, Tatsuya Yamazaki, Jun Matsuda, 電子情報通信学会技術研究報告. ニュ-ロコンピュ-ティング. Neurocomputing . 2000,第688期

机译：一种多智能体强化学习方法，结合其他智能体进行竞技游戏学习
3. A multi-agent reinforcement learning method with learning of other agents for competitive game [J] . Yoichiro Matsuno, Tatsuya Yamazaki, Jun Matsuda, 電子情報通信学会技術研究報告. ニュ-ロコンピュ-ティング. Neurocomputing . 2000,第688期

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4. A Multi-Agent Coordination of a Supply Chain Ordering Management with Multiple Members Using Reinforcement Learning [C] . Ruoying Sun, Gang Zhao, Chunhua Yin IEEE International Conference on Industrial Informatics . 2010

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5. A study of collaborative distributed intelligent multi-agent reinforcement learning via multi goals for dynamic agent shortest path-planning [D] . Kim, Minsuk. 2016

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6. Multi-agent reinforcement learning with approximate model learning for competitive games [O] . Young Joon Park, Yoon Sang Cho, Seoung Bum Kim 2012

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7. Multi-agent reinforcement learning with approximate model learning for competitive games [O] . Young Joon Park, Yoon Sang Cho, Seoung Bum Kim 2019

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8. Multi-Agent Control and Intelligent Sensor Allocation With Reinforcement Learning and Genetic Programming [R] . Gaudiano, P. 2003

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Controlling Multiple Cranes Using Multi-Agent Reinforcement Learning: Emerging Coordination Among Competitive Agents

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