# GeneraLight: Improving Environment Generalization of Traffic Signal Control via Meta Reinforcement Learning

arXiv: https://arxiv.org/abs/2009.08052 · HTML: https://mlsanity.com/paper/2009.08052/

## Key facts

- Cited by 17 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 17 Sep 2020.

## Built on by (most cited first, 17 of 17)

- [Prompt to Transfer: Sim-to-Real Transfer for Traffic Signal Control with Prompt Learning](https://mlsanity.com/paper/2308.14284/) (2023)
- [LibSignal: An Open Library for Traffic Signal Control](https://mlsanity.com/paper/2211.10649/) (2022)
- [X-Light: Cross-City Traffic Signal Control Using Transformer on Transformer as Meta Multi-Agent Reinforcement Learner](https://mlsanity.com/paper/2404.12090/) (2024)
- [ModelLight: Model-Based Meta-Reinforcement Learning for Traffic Signal Control](https://mlsanity.com/paper/2111.08067/) (2021)
- [CoSLight: Co-optimizing Collaborator Selection and Decision-making to Enhance Traffic Signal Control](https://mlsanity.com/paper/2405.17152/) (2024)
- [MetaVIM: Meta Variationally Intrinsic Motivated Reinforcement Learning for Decentralized Traffic Signal Control](https://mlsanity.com/paper/2101.00746/) (2021)
- [CBLab: Supporting the Training of Large-scale Traffic Control Policies with Scalable Traffic Simulation](https://mlsanity.com/paper/2210.00896/) (2022)
- [Improving the generalizability and robustness of large-scale traffic signal control](https://mlsanity.com/paper/2306.01925/) (2023)
- [A Bibliometric Analysis and Review on Reinforcement Learning for Transportation Applications](https://mlsanity.com/paper/2210.14524/) (2022)
- [Using Kolmogorov-Smirnov Distance for Measuring Distribution Shift in Machine Learning](https://mlsanity.com/paper/2510.15996/) (2025)
- [LATS: Large Language Model Assisted Teacher-Student Framework for Multi-Agent Reinforcement Learning in Traffic Signal Control](https://mlsanity.com/paper/2603.24361/) (2026)
- [MTLight: Efficient Multi-Task Reinforcement Learning for Traffic Signal Control](https://mlsanity.com/paper/2404.00886/) (2024)
- [An Offline Meta Black-box Optimization Framework for Adaptive Design of Urban Traffic Light Management Systems](https://mlsanity.com/paper/2408.07327/) (2024)
- [Enhancing Traffic Signal Control through Model-based Reinforcement Learning and Policy Reuse](https://mlsanity.com/paper/2503.08728/) (2025)
- [HALO: Hierarchical Reinforcement Learning for Large-Scale Adaptive Traffic Signal Control](https://mlsanity.com/paper/2506.14391/) (2025)
- [The Distribution Shift Problem in Transportation Networks using Reinforcement Learning and AI](https://mlsanity.com/paper/2509.15291/) (2025)
- [Preference-Conditioned Multi-Objective Reinforcement Learning for Runtime-Tunable Transit Signal Priority](https://mlsanity.com/paper/2607.18286/) (2026)

## Abstract (arXiv)

The heavy traffic congestion problem has always been a concern for modern cities. To alleviate traffic congestion, researchers use reinforcement learning (RL) to develop better traffic signal control (TSC) algorithms in recent years. However, most RL models are trained and tested in the same traffic flow environment, which results in a serious overfitting problem. Since the traffic flow environment in the real world keeps varying, these models can hardly be applied due to the lack of generalization ability. Besides, the limited number of accessible traffic flow data brings extra difficulty in testing the generalization ability of the models. In this paper, we design a novel traffic flow generator based on Wasserstein generative adversarial network to generate sufficient diverse and quality traffic flows and use them to build proper training and testing environments. Then we propose a meta-RL TSC framework GeneraLight to improve the generalization ability of TSC models. GeneraLight boosts the generalization performance by combining the idea of flow clustering and model-agnostic meta-learning. We conduct extensive experiments on multiple real-world datasets to show the superior performance of GeneraLight on generalizing to different traffic flows.