# Multi-Objective Reinforcement Learning for Large-Scale Mixed Traffic Control

arXiv: https://arxiv.org/abs/2512.11247 · HTML: https://mlsanity.com/paper/2512.11247/

## 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 15 of its 40 references that are in the map.
- First posted on arXiv 12 Dec 2025.

## 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, 15 of 15)

- [Rainbow: Combining Improvements in Deep Reinforcement Learning](https://mlsanity.com/paper/1710.02298/) (2017)
- [Route Planning in Transportation Networks](https://mlsanity.com/paper/1504.05140/) (2015)
- [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)
- [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)
- [Mixed Traffic Control and Coordination from Pixels](https://mlsanity.com/paper/2302.09167/) (2023)
- [LASIL: Learner-Aware Supervised Imitation Learning For Long-term Microscopic Traffic Simulation](https://mlsanity.com/paper/2403.17601/) (2024)
- [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)
- [Courteous Behavior of Automated Vehicles at Unsignalized Intersections via Reinforcement Learning](https://mlsanity.com/paper/2106.06369/) (2021)
- [Large-Scale Mixed-Traffic and Intersection Control using Multi-agent Reinforcement Learning](https://mlsanity.com/paper/2504.04691/) (2025)
- [Joint Pedestrian and Vehicle Traffic Optimization in Urban Environments using Reinforcement Learning](https://mlsanity.com/paper/2504.05018/) (2025)
- [Origin-Destination Pattern Effects on Large-Scale Mixed Traffic Control via Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2505.13543/) (2025)
- [MIAT: Maneuver-Intention-Aware Transformer for Spatio-Temporal Trajectory Prediction](https://mlsanity.com/paper/2504.05059/) (2025)

## Abstract (arXiv)

Effective mixed traffic control requires balancing efficiency, fairness, and safety. Existing approaches excel at optimizing efficiency and enforcing safety constraints but lack mechanisms to ensure equitable service, resulting in systematic starvation of vehicles on low-demand approaches. We propose a hierarchical framework combining multi-objective reinforcement learning for local intersection control with strategic routing for network-level coordination. Our approach introduces a Conflict Threat Vector that provides agents with explicit risk signals for proactive conflict avoidance, and a queue parity penalty that ensures equitable service across all traffic streams. Extensive experiments on a real-world network across different robot vehicle (RV) penetration rates demonstrate substantial improvements: up to 53% reductions in average wait time, up to 86% reductions in maximum starvation, and up to 86% reduction in conflict rate compared to baselines, while maintaining fuel efficiency. Our analysis reveals that strategic routing effectiveness scales with RV penetration, becoming increasingly valuable at higher autonomy levels. The results demonstrate that multi-objective optimization through well-curated reward functions paired with strategic RV routing yields significant benefits in fairness and safety metrics critical for equitable mixed-autonomy deployment.