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Hierarchically Structured Scheduling and Execution of Tasks in a Multi-Agent Environment

arXiv 2203.03021 ↗First posted 6 Mar 2022cs.LG cs.MA stat.MLcited by 0

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Abstract (arXiv)

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. Finally, we also conceive the case where centralisation is impossible at test time and workers must learn how to cooperate in executing the tasks in an environment with no schedule and only partial observability.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 34 of its 67 references that are in the map. First posted on arXiv 6 Mar 2022.

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34 of 67 references.

References · 34

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22 more
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