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

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.