论文标题
多代理强化学习和审查其在自主移动性中的应用的简介
An Introduction to Multi-Agent Reinforcement Learning and Review of its Application to Autonomous Mobility
论文作者
论文摘要
流动性和流量的许多场景都涉及多种不同的代理，需要合作以找到共同解决方案。行为计划的最新进展使用强化学习以寻找有效和表现的行为策略。但是，随着自动驾驶汽车和车辆对X通信变得越来越成熟，只有使用单一独立代理的解决方案在道路上留下了潜在的性能增长。多代理增强学习（MARL）是一个研究领域，旨在为彼此相互作用的多种代理找到最佳解决方案。这项工作旨在将该领域的概述介绍给研究人员的自主行动能力。我们首先解释Marl并介绍重要概念。然后，我们讨论基于Marl算法的主要范式，并概述每个范式中最先进的方法和思想。在这种背景下，我们调查了MAL在自动移动性场景中的应用程序，并概述了现有的场景和实现。
Many scenarios in mobility and traffic involve multiple different agents that need to cooperate to find a joint solution. Recent advances in behavioral planning use Reinforcement Learning to find effective and performant behavior strategies. However, as autonomous vehicles and vehicle-to-X communications become more mature, solutions that only utilize single, independent agents leave potential performance gains on the road. Multi-Agent Reinforcement Learning (MARL) is a research field that aims to find optimal solutions for multiple agents that interact with each other. This work aims to give an overview of the field to researchers in autonomous mobility. We first explain MARL and introduce important concepts. Then, we discuss the central paradigms that underlie MARL algorithms, and give an overview of state-of-the-art methods and ideas in each paradigm. With this background, we survey applications of MARL in autonomous mobility scenarios and give an overview of existing scenarios and implementations.
