Can RL agents learn to eco-drive in real-world traffic?
INTERSECTIONZOO: ECO-DRIVING FOR BENCHMARKING MULTI-AGENT CONTEXTUAL REINFORCEMENT LEARNING
October 22, 2024
https://arxiv.org/pdf/2410.15221This paper introduces IntersectionZoo, a benchmark designed to test how well multi-agent AI systems, particularly those using reinforcement learning (RL), can adapt to changing real-world conditions. It focuses on the problem of coordinating a fleet of vehicles for fuel efficiency (eco-driving) at traffic intersections, a complex task involving uncertainty from other drivers.
Key points:
- Real-world focus: Unlike many benchmarks that use simplified simulations, IntersectionZoo uses data from real intersections, traffic patterns, and factors like weather to create a more realistic challenge.
- Contextual Reinforcement Learning (CRL): The benchmark emphasizes the ability of AI agents to generalize their learning to new but similar situations (different intersections, traffic conditions, etc.), which is crucial for real-world deployment.
- Multi-Objective: IntersectionZoo considers not just emissions, but also factors like travel time and safety, reflecting the complex trade-offs in real-world applications.
- Evaluating Common Algorithms: The paper demonstrates that several popular multi-agent RL algorithms struggle to perform well in IntersectionZoo, highlighting the need for more research in this area.
- Open for Research: IntersectionZoo provides an open-source platform with tools and realistic data, allowing other researchers to test and develop better multi-agent AI algorithms for complex real-world problems.