论文标题
部分可观测时空混沌系统的无模型预测
Reinforcement Learning Approach for Multi-Agent Flexible Scheduling Problems
论文作者
论文摘要
安排在自动生产中起着重要作用。它的影响可以在制造业，服务行业和技术行业等各个领域找到。调度问题（NP-HARD）是在给定的一组计算机上找到一系列作业分配的任务，目的是优化目标定义的目标。诸如操作研究，调度规则和组合优化之类的方法已应用于调度问题，但没有解决方案保证找到最佳解决方案。强化学习的最新发展显示在顺序决策问题中取得了成功。这项研究提出了用于调度问题的强化学习方法。特别是，这项研究提供了一个开放式健身房环境，并为车间调度问题降低了搜索空间，并提供了一种启发式指导的Q学习解决方案，并具有最先进的性能，可用于多代理灵活的车间问题。
Scheduling plays an important role in automated production. Its impact can be found in various fields such as the manufacturing industry, the service industry and the technology industry. A scheduling problem (NP-hard) is a task of finding a sequence of job assignments on a given set of machines with the goal of optimizing the objective defined. Methods such as Operation Research, Dispatching Rules, and Combinatorial Optimization have been applied to scheduling problems but no solution guarantees to find the optimal solution. The recent development of Reinforcement Learning has shown success in sequential decision-making problems. This research presents a Reinforcement Learning approach for scheduling problems. In particular, this study delivers an OpenAI gym environment with search-space reduction for Job Shop Scheduling Problems and provides a heuristic-guided Q-Learning solution with state-of-the-art performance for Multi-agent Flexible Job Shop Problems.
