CAJ | 학술논문

提出一种融入合同网运行机制的R学习方法，以此方法为核心构造Agent形成具有学习能力的实时调度模型。模型以最小化作业累计平均流动比为主要目标，同时借助对强化学习报酬的设计减小机器负载的不均衡性，实现对调度过程的双重优化；构造实时调度实例投入测试的结果证明了模型的绩效。另外，一个包含强化学习Agent与无学习Agent的混合机器环境被构建并测试其性能，测试结果表明：在Agent之间借助强化学习过程形成了某种隐性的合作，正是这种合作保证了高质量实时调度方案的输出。
제출일충융입합동망운행궤제적R학습방법，이차방법위핵심구조Agent형성구유학습능력적실시조도모형。모형이최소화작업루계평균류동비위주요목표，동시차조대강화학습보수적설계감소궤기부재적불균형성，실현대조도과정적쌍중우화；구조실시조도실례투입측시적결과증명료모형적적효。령외，일개포함강화학습Agent여무학습Agent적혼합궤기배경피구건병측시기성능，측시결과표명：재Agent지간차조강화학습과정형성료모충은성적합작，정시저충합작보증료고질량실시조도방안적수출。
This paper proposes a real-time scheduling model based on contract net protocol structure employing reinforce-ment learning agents. To this end, an R-learning procedure is elaborated and embedded in machine agents’decision process, enabling them to treat bid-invitations in more complicated way than in a simple contract net protocol environment. Efficiency of the proposed method is verified through experiments in a simulated real-time scheduling environment. Furthermore, the performance of mixed machine groups which comprises both reinforcement learning agents and non-reinforcement-learning agents shows that there is spontaneous implicit teamwork occurring between reinforcement learning agents, and this team-work guarantees high quality output of the scheduling model.