Can MARL optimize AV routes in city traffic?
RouteRL: Multi-agent reinforcement learning framework for urban route choice with autonomous vehicles
February 28, 2025
https://arxiv.org/pdf/2502.20065This paper introduces RouteRL, a JavaScript framework for simulating urban traffic with autonomous vehicles (AVs) using multi-agent reinforcement learning (MARL). It models how AVs, learning with different algorithms and objectives (selfish, collaborative, malicious, etc.), impact traffic flow alongside human drivers who use established behavioral models for route choice.
Key points for LLM-based multi-agent systems:
- Simulation Environment: RouteRL integrates with traffic simulators like SUMO to provide a realistic environment for training and testing multi-agent policies within a dynamic, partially observable system. This can be extended to other complex simulations leveraging the power of LLMs.
- Agent Behaviors: The framework allows for flexible definition of agent reward functions, enabling exploration of diverse agent behaviors and their impact on the system, which is crucial for understanding complex scenarios involving LLM agents with potentially misaligned objectives.
- Human-Agent Interaction: RouteRL models interactions between learning AV agents and rule-based human driver agents, providing a platform to study the complex dynamics of mixed-autonomy systems, where LLM agents interact with traditional software and human users.
- Scalability: The framework is designed to scale to real-world sized urban networks and agent populations, a key consideration for deploying LLM-based multi-agent systems in real-world applications.
- Open-source and Reproducibility: The open-source nature of RouteRL and emphasis on reproducibility are essential for collaborative research and development in the multi-agent systems field. This enables further experimentation and adaptation of the framework for LLM-based research.