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

03/06/2022
by   Diogo S. Carvalho, et al.
0

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.

READ FULL TEXT
research
06/06/2021

ScheduleNet: Learn to solve multi-agent scheduling problems with reinforcement learning

We propose ScheduleNet, a RL-based real-time scheduler, that can solve v...
research
11/12/2018

Coordinating Disaster Emergency Response with Heuristic Reinforcement Learning

A crucial and time-sensitive task when any disaster occurs is to rescue ...
research
06/09/2019

Neural Heterogeneous Scheduler

Access to parallel and distributed computation has enabled researchers a...
research
06/08/2012

A Distributed Optimized Patient Scheduling using Partial Information

A software agent may be a member of a Multi-Agent System (MAS) which is ...
research
03/07/2022

Reinforcement Learning for Location-Aware Scheduling

Recent techniques in dynamical scheduling and resource management have f...
research
10/11/2020

A Feedback Scheme to Reorder a Multi-Agent Execution Schedule by Persistently Optimizing a Switchable Action Dependency Graph

In this paper we consider multiple Automated Guided Vehicles (AGVs) navi...
research
03/26/2021

ReaDmE: Read-Rate Based Dynamic Execution Scheduling for Intermittent RF-Powered Devices

This paper presents a method for remotely and dynamically determining th...

Please sign up or login with your details

Forgot password? Click here to reset