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Why the 'selfish' optimizing agents could solve the decentralized reinforcement learning problems

机译:为什么“自私”的优化代理可以解决分散式强化学习问题

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摘要

Multidisciplinary Design Optimization (MDO) is a computational approach for optimizing design of a complex system of systems that require knowledge from multiple disciplines. In a former study, we explored and found that the individual discipline feasible (IDF), a type of MDO design technique, performed well in several benchmark test cases of decentralized Reinforcement Learning (RL) problems, in particular, stabilizing an unknown system. However, the earlier study was not able to resolve as to why the overall system of systems, even with strongly coupled systems, could be stabilized when each agent just focused on stabilizing itself. In this work, we make significant extension in resolving this behavior by conducting a theoretical analysis of the MDO solution of RL problems. Through the analysis, we show that with the proper control law, each MDO agent should be able to bring its state closer to the 0-stable point regardless of how the other agents' states impact the state of the whole system. This is the main reason why the 'selfish' MDO-IDF agents are successful in learning to stabilize the overall system. The simulation results, including benchmark test cases, verify our analysis. Therefore, we propose that the MDO would be a promising solution in many other decentralized RL problems.
机译:多学科设计优化(MDO)是一种计算方法,用于优化需要来自多个学科的知识的复杂系统的设计。在以前的研究中,我们探索并发现,个体学科可行性(IDF)是一种MDO设计技术,在分散式强化学习(RL)问题的多个基准测试案例中表现良好,尤其是稳定了未知系统。但是,较早的研究无法确定为什么当每个代理仅专注于稳定自身时,即使具有强耦合的系统也可以稳定整个系统。在这项工作中,我们通过对RL问题的MDO解决方案进行理论分析,为解决此问题做出了重大扩展。通过分析,我们表明,借助适当的控制律,每个MDO代理都应能够使其状态更接近0稳定点,而不管其他代理的状态如何影响整个系统的状态。这是“自私” MDO-IDF代理成功学习稳定整个系统的主要原因。仿真结果(包括基准测试用例)验证了我们的分析。因此,我们建议MDO将是解决许多其他分散式RL问题的有希望的解决方案。

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