用多智能体强化学习优化自动驾驶车辆路径选择
RouteRL: Multi-agent reinforcement learning framework for urban route choice with autonomous vehicles
- 将MARL与微观交通仿真结合，模拟城市路网中人车共行
- 自动驾驶车辆作为强化学习智能体，自主优化出行策略
- 适合研究智能交通、人机协同与多智能体学习的学者
RouteRL是一个创新框架，融合多智能体强化学习（MARL）与微观交通仿真，用于测试和开发自动驾驶车辆（AVs）的高效路径选择策略。该框架模拟城市中驾驶员代理的日常路径选择，包含两类：使用行为路径选择模型模拟的人类驾驶员，以及以MARL代理形式建模、针对预设目标优化策略的自动驾驶车辆。本研究提供路由决策技术报告，阐述其在多智能体强化学习、交通建模及人-人工智能交互方面的潜在研究贡献，并通过实例展示其应用影响。
原文摘要 · Abstract (English)
RouteRL is a novel framework that integrates multi-agent reinforcement learning (MARL) with a microscopic traffic simulation, facilitating the testing and development of efficient route choice strategies for autonomous vehicles (AVs). The proposed framework simulates the daily route choices of driver agents in a city, including two types: human drivers, emulated using behavioral route choice models, and AVs, modeled as MARL agents optimizing their policies for a predefined objective. RouteRL aims to advance research in MARL, transport modeling, and human-AI interaction for transportation applications. This study presents a technical report on RouteRL, outlines its potential research contributions, and showcases its impact via illustrative examples.
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