IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement Learning

Despite the popularity of multi-agent reinforcement learning (RL) in simulated and two-player applications, its success in messy real-world applications has been limited. A key challenge lies in its generalizability across problem variations, a common necessity for many real-world problems. Contextual reinforcement learning (CRL) formalizes learning policies that generalize across problem variations. However, the lack of standardized benchmarks for multi-agent CRL has hindered progress in the field. Such benchmarks are desired to be based on real-world applications to naturally capture the many open challenges of real-world problems that affect generalization. To bridge this gap, we propose IntersectionZoo, a comprehensive benchmark suite for multi-agent CRL through the real-world application of cooperative eco-driving in urban road networks. The task of cooperative eco-driving is to control a fleet of vehicles to reduce fleet-level vehicular emissions. By grounding IntersectionZoo in a real-world application, we naturally capture real-world problem characteristics, such as partial observability and multiple competing objectives. IntersectionZoo is built on data-informed simulations of 16,334 signalized intersections derived from 10 major US cities, modeled in an open-source industry-grade microscopic traffic simulator. By modeling factors affecting vehicular exhaust emissions (e.g., temperature, road conditions, travel demand), IntersectionZoo provides one million data-driven traffic scenarios. Using these traffic scenarios, we benchmark popular multi-agent RL and human-like driving algorithms and demonstrate that the popular multi-agent RL algorithms struggle to generalize in CRL settings. Having demonstrated impressive performance in simulated multi-agent applications such as Starcraft (Samvelyan et al., 2019), RL holds potential for various multi-agent real-world applications including autonomous driving (Kiran et al., 2021), robotic warehousing (Bahrpeyma & Reichelt, 2022), and traffic control (Wu et al., 2021). However, compared to simulated applications, the success of RL in real-world applications has been rather limited (Dulac-Arnold et al., 2021). A key challenge lies in making RL algorithms generalize across problem variations, such as when weather conditions change in autonomous driving.