Hierarchically Structured Scheduling and Execution of Tasks in a Multi-Agent Environment
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by
Diogo S. Carvalho, Biswa Sengupta
2022
Abstract
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.
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