Multi-Agent Proximal Policy Optimization for a Deadlock Capable Transport System in a Simulation-Based Learning Environment

Winter Simulation Conference, pp. 1818–1829

Abstract

In this paper, we explore the potential of multi-agent reinforcement learning (MARL) for managing the driving behavior of autonomous guided vehicles (AGVs) in production logistics environments with single-lane tracks, where deadlocks pose a significant challenge. We build upon previous work and adopt a MARL approach using the Proximal Policy Optimization (PPO) algorithm. We conduct a thorough hyperparameter search and investigate the impact of varying numbers of agents on the performance of the AGVs. Our results demonstrate the effectiveness of the MARL approach in addressing deadlocks and coordinating AGV behavior, as well as the scalability of the learned policy to different numbers of agents. The Bayesian optimization process and increased iteration count contribute to improved performance and more stable learning curves.

Authors 4

1. Marcel Müller

   Otto-von-Guericke-Universität Magdeburg

   Affiliation as printed

   Otto von Guericke University Magdeburg,Institute of Logistics and Material Handling Systems,Magdeburg,Germany,39106

2. Lorena Silvana Reyes-Rubiano Aachen

   RWTH Aachen University

   Affiliation as printed

   RWTH Aachen University,Data & Business Analytics,Aachen,Germany,52072

3. Tobias Reggelin

   Otto-von-Guericke-Universität Magdeburg

   Affiliation as printed

   Otto von Guericke University Magdeburg,Institute of Logistics and Material Handling Systems,Magdeburg,Germany,39106

4. Hartmut Zadek

   Otto-von-Guericke-Universität Magdeburg

   Affiliation as printed

   Otto von Guericke University Magdeburg,Institute of Logistics and Material Handling Systems,Magdeburg,Germany,39106