mlsanityData to 7 Oct 2026

RouteRL: Multi-agent reinforcement learning framework for urban route choice with autonomous vehicles

arXiv 2502.20065 ↗First posted 27 Feb 2025cs.MA cs.LGcited by 2

On the map: End-to-end autonomous driving → Reinforcement learning for driving

Abstract (arXiv)

RouteRL is a novel framework that integrates multi-agent reinforcement learning (MARL) with a microscopic traffic simulation, facilitating the testing and development of efficient route choice strategies for autonomous vehicles (AVs). The proposed framework simulates the daily route choices of driver agents in a city, including two types: human drivers, emulated using behavioral route choice models, and AVs, modeled as MARL agents optimizing their policies for a predefined objective. RouteRL aims to advance research in MARL, transport modeling, and human-AI interaction for transportation applications. This study presents a technical report on RouteRL, outlines its potential research contributions, and showcases its impact via illustrative examples.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Placed in Reinforcement learning for driving, one of 9 directions in End-to-end autonomous driving. Builds on 13 of its 29 references that are in the map. First posted on arXiv 27 Feb 2025.

Builds on

13 of 29 references.

References · 13

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2021The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Gamescited by 903
  3. 2020Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learningcited by 475
  4. 2020Is Independent Learning All You Need in the StarCraft Multi-Agent Challenge?cited by 281
  5. 2020PettingZoo: Gym for Multi-Agent Reinforcement Learningcited by 209
  6. 2020Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Taskscited by 207
  7. 2020SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Drivingcited by 121
  8. 2017Emergent Complexity via Multi-Agent Competitioncited by 117
  9. 2019CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenariocited by 114
  10. 2023TorchRL: A data-driven decision-making library for PyTorchcited by 59
  11. 2020Neural MMO v1.3: A Massively Multiagent Game Environment for Training and Evaluating Neural Networkscited by 3
  12. 2023Wardrop Equilibrium Can Be Boundedly Rational: A New Behavioral Theory of Route Choicecited by 2
1 more
  1. 2024Social impact of CAVs -- coexistence of machines and humans in the context of route choicecited by 2

Built on by

Cited by year

Citing papers per year: 2025: 2.

2 papers.

  1. 2025URB -- Urban Routing Benchmark for RL-equipped Connected Autonomous Vehiclescited by 3
  2. 2025Autonomous vehicles need social awareness to find optima in multi-agent reinforcement learning routing gamescited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Multi-Agent Reinforcement Learning for Autonomous Driving: A Survey
  2. 2025Impact of Collective Behaviors of Autonomous Vehicles on Urban Traffic Dynamics: A Multi-Agent Reinforcement Learning Approach
  3. 2025Collaboration Between the City and Machine Learning Community is Crucial to Efficient Autonomous Vehicles Routing
  4. 2022ApolloRL: a Reinforcement Learning Platform for Autonomous Driving
  5. 2021Multi-agent Reinforcement Learning for Cooperative Lane Changing of Connected and Autonomous Vehicles in Mixed Traffic
  6. 2023Action and Trajectory Planning for Urban Autonomous Driving with Hierarchical Reinforcement Learning
  7. 2026Multi-Agent Reinforcement Learning for Safe Autonomous Driving Under Pedestrian Behavioral Uncertainty
  8. 2025LearningFlow: Automated Policy Learning Workflow for Urban Driving with Large Language Models