The surge in Reinforcement Learning (RL) applications in Intelligent Transportation Systems (ITS) has contributed to its growth as well as highlighted key challenges. However, defining objectives of RL agents in traffic control and management tasks, as well as aligning policies with these goals through an effective formulation of Markov Decision Process (MDP), can be challenging and often require domain experts in both RL and ITS. Recent advancements in Large Language Models (LLMs) such as GPT-4 highlight their broad general knowledge, reasoning capabilities, and commonsense priors across various domains. In this work, we conduct a large-scale user study involving 70 participants to investigate whether novices can leverage ChatGPT to solve complex mixed traffic control problems. Three environments are tested, including ring road, bottleneck, and intersection. We find ChatGPT has mixed results. For intersection and bottleneck, ChatGPT increases number of successful policies by 150% and 136% compared to solely beginner capabilities, with some of them even outperforming experts. However, ChatGPT does not provide consistent improvements across all scenarios.

翻译：强化学习在智能交通系统中的广泛应用既推动了其发展，也凸显了关键挑战。然而，在交通控制与管理任务中定义强化学习智能体的目标，并通过有效构建马尔可夫决策过程使其策略与这些目标对齐，通常极具挑战性，往往需要同时精通强化学习和智能交通系统的领域专家。近年来，以GPT-4为代表的大型语言模型在跨领域通用知识、推理能力及常识先验方面展现出显著优势。本研究通过一项涉及70名参与者的大规模用户实验，探究非专业人员能否借助ChatGPT解决复杂的混合交通控制问题。实验测试了环形道路、交通瓶颈和交叉路口三种场景。结果表明ChatGPT的效果存在差异性：在交叉路口和交通瓶颈场景中，ChatGPT使用户成功策略数量分别较纯新手基线提升150%和136%，部分策略甚至优于专家水平；但该模型并未在所有场景中持续提升性能。