基于协同强化学习的多策略优化研究问题

Ivana Dusparic, V. Cahill
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引用次数: 8

摘要

自组织技术已被成功地用于优化软件系统,例如在自组织网络路由中优化路由稳定性,以及使用负载平衡优化存储空间或处理能力的使用。现有的自组织技术通常关注单个(通常隐式指定)系统目标,并调整系统参数以最佳地满足该目标。本文研究大规模多智能体泛在计算环境下的优化问题,如城市交通控制问题。这类应用程序通常需要同时针对多个目标进行优化。此外,这些多个目标可能会相互冲突,随着时间的推移而变化,并应用于系统的各个部分,例如单个代理、一组代理或整个系统。与现有的自组织系统相比,在这些系统中,智能体是同质的,因为它们朝着一个共同的目标努力,而这些系统中的智能体是异质的,因为它们可能有不同的目标。因此,现有的自组织优化技术必须扩展到处理多目标优化和由此产生的代理异质性。在本文中,我们提出了扩展协作强化学习(CRL)的研究议程,这是一种现有的自组织优化技术,以支持多策略优化。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Research Issues in Multiple Policy Optimization Using Collaborative Reinforcement Learning
Self-organizing techniques have successfully been used to optimize software systems, such as optimization of route stability in ad hoc network routing and optimization of the use of storage space or processing power using load balancing. Existing self-organizing techniques typically focus on a single, usually implicitly specified, system goal and tune systems parameters towards optimally meeting that goal. In this paper, we consider optimization of large-scale multi-agent ubiquitous computing environments, such as urban traffic control. Applications in this class are typically required to optimize towards multiple goals simultaneously. Additionally, these multiple goals can potentially be conflicting, change over time, and apply to various parts of the system such as a single agent, a group of agents, or the system as a whole. In contrast to existing self-organizing systems in which agents are homogeneous to the extent that they are working towards a common goal, agents in these systems are heterogeneous in that they may have differing goals. Thus, existing self-organizing optimization techniques must be extended to deal with multiple goal optimization and the resulting heterogeneity of agents. In this paper we present a research agenda for extending collaborative reinforcement learning (CRL), an existing self-organizing optimization technique, to support multiple policy optimization.
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