# Neighbor-Aware Reinforcement Learning for Mixed Traffic Optimization in Large-scale Networks

arXiv: https://arxiv.org/abs/2412.12622 · HTML: https://mlsanity.com/paper/2412.12622/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods.
- Builds on 16 of its 39 references that are in the map.
- First posted on arXiv 17 Dec 2024.

## Placed in

- Deep reinforcement learning methods / [RL for autonomous driving and traffic](https://mlsanity.com/deeprl/rl-for-autonomous-driving-and-traffic/)

## Builds on (most cited first, 16 of 16)

- [Rainbow: Combining Improvements in Deep Reinforcement Learning](https://mlsanity.com/paper/1710.02298/) (2017)
- [Can ChatGPT Enable ITS? The Case of Mixed Traffic Control via Reinforcement Learning](https://mlsanity.com/paper/2306.08094/) (2023)
- [Reinforcement Learning for Mixed Autonomy Intersections](https://mlsanity.com/paper/2111.04686/) (2021)
- [ADAPS: Autonomous Driving Via Principled Simulations](https://mlsanity.com/paper/1907.08874/) (2019)
- [Learning to Control and Coordinate Mixed Traffic Through Robot Vehicles at Complex and Unsignalized Intersections](https://mlsanity.com/paper/2301.05294/) (2023)
- [Large-scale Mixed Traffic Control Using Dynamic Vehicle Routing and Privacy-Preserving Crowdsourcing](https://mlsanity.com/paper/2311.11347/) (2023)
- [A Decentralized Energy-Optimal Control Framework for Connected Automated Vehicles at Signal-Free Intersections](https://mlsanity.com/paper/1602.03786/) (2016)
- [Mixed Traffic Control and Coordination from Pixels](https://mlsanity.com/paper/2302.09167/) (2023)
- [EnduRL: Enhancing Safety, Stability, and Efficiency of Mixed Traffic Under Real-World Perturbations Via Reinforcement Learning](https://mlsanity.com/paper/2311.12261/) (2023)
- [Predicting Station-Level Bike-Sharing Demands Using Graph Convolutional Neural Network](https://mlsanity.com/paper/2004.08723/) (2020)
- [A Generic Graph Sparsification Framework using Deep Reinforcement Learning](https://mlsanity.com/paper/2112.01565/) (2021)
- [LASIL: Learner-Aware Supervised Imitation Learning For Long-term Microscopic Traffic Simulation](https://mlsanity.com/paper/2403.17601/) (2024)
- [Black-box Adversarial Attacks on Network-wide Multi-step Traffic State Prediction Models](https://mlsanity.com/paper/2110.08712/) (2021)
- [CARL: Congestion-Aware Reinforcement Learning for Imitation-based Perturbations in Mixed Traffic Control](https://mlsanity.com/paper/2404.00796/) (2024)
- [Heterogeneous Mixed Traffic Control and Coordination](https://mlsanity.com/paper/2409.12330/) (2024)
- [Learning to Control DC Motor for Micromobility in Real Time with Reinforcement Learning](https://mlsanity.com/paper/2108.00138/) (2021)

## Abstract (arXiv)

Managing mixed traffic comprising human-driven and robot vehicles (RVs) across large-scale networks presents unique challenges beyond single-intersection control. This paper proposes a reinforcement learning framework for coordinating mixed traffic across multiple interconnected intersections. Our key contribution is a neighbor-aware reward mechanism that enables RVs to maintain balanced distribution across the network while optimizing local intersection efficiency. We evaluate our approach using a real-world network, demonstrating its effectiveness in managing realistic traffic patterns. Results show that our method reduces average waiting times by 39.2% compared to the state-of-the-art single-intersection control policy and 79.8% compared to traditional traffic signals. The framework's ability to coordinate traffic across multiple intersections while maintaining balanced RV distribution provides a foundation for deploying learning-based solutions in urban traffic systems.