A MAS Reinforcement Learning Approach for Indeterministic Multi-Layer Job-Shop Scheduling
نویسندگان
چکیده
The indeterministic multi-layer job-shop scheduling problem, which is the extension of the traditional job-shop scheduling, is introduced in this paper. The framework and some key issues of the problem are discussed. A multi-agent reinforcement learning approach, named memory-evolution-based MAS reinforcement learning algorithm, is breifly introduced too. Experiment results show that our approach is more benificial than other two approaches and it is roubust even when the system faces unexpected breakdown.
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