Federated Deep Reinforcement Learning for Task Scheduling in Heterogeneous Autonomous Robotic System

Abstract

In this paper, we investigate the problem of task scheduling in automated warehouses with hetero-geneous autonomous robotic systems. We formulate the task scheduling for a heterogeneous autonomous robots (HAR) system in each warehouse as a queueing control optimization problem in which we aim to minimize the queue length of tasks that are waiting to be processed. We propose a deep reinforcement learning (DRL) based approach that employs the proximal policy optimization (PPO) to achieve an optimal task scheduling policy. We then propose a federated learning based algorithm to improve the performance of the PPO agents. The simulation results fully demonstrate the performance improvement of our proposed algorithm in terms of average queue length compared to the distributed learning algorithm.