The Efficiency of Reinforcement Learning: 007 or Johnny English? Can Reinforcement Learning Agents Outperform Deterministic Algorithms?

This study aims to investigate the efficacy of Deep Reinforcement Learning (DRL) in the domain of industrial motion control, especially path planning, and scheduling. In order to abstract challenges that can be found in industrial path planning and scheduling operations, we construct a simulation of a generic logistical use case for an AGV. The experiments are centered on evaluating the performance of PPO, benchmarked against a deterministic agent in controlling an AGV for optimal scheduling and routing decisions. The agents’ control relates to finding the optimal scheduling strategy of machines, while efficiently navigating in a complex operational framework under risk of disruptions and time-out events. We compare the characteristics of deterministic algorithms to DRL, contrast the results of the experiment, and derive implications for the industries
Masterarbeit Universität Innsbruck 2024