# CoSLight: Co-optimizing Collaborator Selection and Decision-making to Enhance Traffic Signal Control

arXiv: https://arxiv.org/abs/2405.17152 · HTML: https://mlsanity.com/paper/2405.17152/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 22 references that are in the map.
- First posted on arXiv 27 May 2024.

## Builds on (most cited first, 13 of 13)

- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [Counterfactual Multi-Agent Policy Gradients](https://mlsanity.com/paper/1705.08926/) (2017)
- [Multi-Agent Reinforcement Learning is a Sequence Modeling Problem](https://mlsanity.com/paper/2205.14953/) (2022)
- [CoLight: Learning Network-level Cooperation for Traffic Signal Control](https://mlsanity.com/paper/1905.05717/) (2019)
- [Inductive Graph Neural Networks for Spatiotemporal Kriging](https://mlsanity.com/paper/2006.07527/) (2020)
- [Learning Phase Competition for Traffic Signal Control](https://mlsanity.com/paper/1905.04722/) (2019)
- [AttendLight: Universal Attention-Based Reinforcement Learning Model for Traffic Signal Control](https://mlsanity.com/paper/2010.05772/) (2020)
- [Prompt to Transfer: Sim-to-Real Transfer for Traffic Signal Control with Prompt Learning](https://mlsanity.com/paper/2308.14284/) (2023)
- [Neighborhood Cognition Consistent Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/1912.01160/) (2019)
- [GeneraLight: Improving Environment Generalization of Traffic Signal Control via Meta Reinforcement Learning](https://mlsanity.com/paper/2009.08052/) (2020)
- [SafeLight: A Reinforcement Learning Method toward Collision-free Traffic Signal Control](https://mlsanity.com/paper/2211.10871/) (2022)
- [DynSTGAT: Dynamic Spatial-Temporal Graph Attention Network for Traffic Signal Control](https://mlsanity.com/paper/2109.05491/) (2021)
- [HumanLight: Incentivizing Ridesharing via Human-centric Deep Reinforcement Learning in Traffic Signal Control](https://mlsanity.com/paper/2304.03697/) (2023)

## Built on by (most cited first, 7 of 7)

- [MalLight: Influence-Aware Coordinated Traffic Signal Control for Traffic Signal Malfunctions](https://mlsanity.com/paper/2408.09768/) (2024)
- [Unicorn: A Universal and Collaborative Reinforcement Learning Approach Towards Generalizable Network-Wide Traffic Signal Control](https://mlsanity.com/paper/2503.11488/) (2025)
- [NGENT: Next-Generation AI Agents Must Integrate Multi-Domain Abilities to Achieve Artificial General Intelligence](https://mlsanity.com/paper/2504.21433/) (2025)
- [GuideLight: "Industrial Solution" Guidance for More Practical Traffic Signal Control Agents](https://mlsanity.com/paper/2407.10811/) (2024)
- [HALO: Hierarchical Reinforcement Learning for Large-Scale Adaptive Traffic Signal Control](https://mlsanity.com/paper/2506.14391/) (2025)
- [DGLight: DQN-Guided GRPO Fine-Tuning of Large Language Models for Traffic Signal Control](https://mlsanity.com/paper/2604.25259/) (2026)
- [VLALight: A Vision-Language-Action Model for Traffic Signal Control](https://mlsanity.com/paper/2609.36934/) (2026)

## Abstract (arXiv)

Effective multi-intersection collaboration is pivotal for reinforcement-learning-based traffic signal control to alleviate congestion. Existing work mainly chooses neighboring intersections as collaborators. However, quite an amount of congestion, even some wide-range congestion, is caused by non-neighbors failing to collaborate. To address these issues, we propose to separate the collaborator selection as a second policy to be learned, concurrently being updated with the original signal-controlling policy. Specifically, the selection policy in real-time adaptively selects the best teammates according to phase- and intersection-level features. Empirical results on both synthetic and real-world datasets provide robust validation for the superiority of our approach, offering significant improvements over existing state-of-the-art methods. The code is available at https://github.com/bonaldli/CoSLight.