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

• URL: http://arxiv.org/abs/2410.15221v1
• Date: Sat, 19 Oct 2024 21:34:24 GMT
• Title: IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement Learning
• Authors: Vindula Jayawardana, Baptiste Freydt, Ao Qu, Cameron Hickert, Zhongxia Yan, Cathy Wu,
• Abstract summary: We propose IntersectionZoo, a comprehensive benchmark suite for multi-agent reinforcement learning. By grounding IntersectionZoo in a real-world application, we naturally capture real-world problem characteristics. IntersectionZoo is built on data-informed simulations of 16,334 signalized intersections from 10 major US cities.
• Score: 4.80862277413422
• License:
• Abstract: 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.

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