An efficient and adaptive design of reinforcement learning environment to solve job shop scheduling problem with soft actor-critic algorithm
设计了一个高效、鲁棒且与规模无关的强化学习环境，采用集中训练分散执行的多智能体架构，结合软演员-评论家算法，在作业车间调度问题上优于传统调度规则和其他算法。
Shop scheduling is deeply involved in manufacturing. In order to improve the efficiency of scheduling and fit dynamic scenarios, many Deep Reinforcement Learning (DRL) methods are studied to solve scheduling problems like job shop and flow shop. But most studies focus on using the latest algorithms while ignoring that the environment plays an important role in agent learning. In this paper, we design an effective, robust and size-agnostic environment for job shop scheduling. The proposed design of environment uses centralised training and decentralised execution (CTDE) to implement a multi-agent architecture. Together with the observation space we design, environmental information that is irrelevant to the current decision is eliminated as much as possible. The proposed action space enlarges the decision space of agents, which performs better than the traditional way. Finally, Soft Actor-Critic (SAC) algorithm is adapted to learning within this environment. By comparing with traditional scheduling rules, other reinforcement learning algorithms, and relevant literature, the superiority of the results obtained in this study is demonstrated.