In a warehouse environment, tasks appear dynamically. Consequently, a task management system that matches them with the workforce too early (e.g., weeks in advance) is necessarily sub-optimal. Also, the rapidly increasing size of the action space of such a system consists of a significant problem for traditional schedulers. Reinforcement learning, however, is suited to deal with issues requiring making sequential decisions towards a long-term, often remote, goal. In this work, we set ourselves on a problem that presents itself with a hierarchical structure: the task-scheduling, by a centralised agent, in a dynamic warehouse multi-agent environment and the execution of one such schedule, by decentralised agents with only partial observability thereof. We propose to use deep reinforcement learning to solve both the high-level scheduling problem and the low-level multi-agent problem of schedule execution. The topic and contribution is relevant to both reinforcement learning and operations research scientific communities and is directed towards future real-world industrial applications.
CITATION STYLE
Carvalho, D., & Sengupta, B. (2022). Hierarchically Structured Scheduling and Execution of Tasks in a Multi-agent Environment. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 13566 LNAI, pp. 15–26). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-16474-3_2
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