论文标题
层次结构化的调度和执行任务在多代理环境中
Hierarchically Structured Scheduling and Execution of Tasks in a Multi-Agent Environment
论文作者
论文摘要
在仓库环境中，任务动态出现。因此，一个使他们与劳动力队伍相匹配的任务管理系统（例如，提前几周）必然是最佳的。同样，这种系统的动作空间的迅速增加包括传统调度程序的重大问题。但是，强化学习适合处理需要长期（通常是遥远）目标做出顺序决策的问题。在这项工作中，我们将自己设置为一个具有分层结构的问题：集中式代理，在动态仓库多代理环境中进行任务安排，并通过其仅具有部分可观察性的分散式代理，并执行一个这样的时间表。我们建议使用深度强化学习来解决高级调度问题和时间表执行的低级多代理问题。最后，我们还构想了在测试时间不可能进行集中化的情况，并且工人必须学习如何在没有时间表，只有部分可观察性的环境中执行任务。
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.
