Reinforcement Learning with Discrete Event Simulation: The Premise, Reality, and Promise

摘要

Several studies have shown the success of Reinforcement Learning (RL) for solving sequential decision-making problems in domains like robotics, autonomous vehicles, manufacturing, supply chain, and health care. For such applications, uncertainty in real-life environments presents a significant challenge in training an RL agent. RL requires a large number of trials (training examples) to learn a good policy. One of the approaches to tackle these obstacles is augmenting RL with a Discrete Event Simulation (DES) model. Learning from a simulated environment, makes the training process of the RL agent more efficient, faster, and even safer by alleviating the need for expensive real-world trials. Therefore, integrating RL algorithms with simulation environments has inspired many researchers in recent years. In this paper, we analyze the existing literature on RL models using DES to put forward the benefits, application areas, challenges, and scope for future work in developing such models for industrial use cases.