# AttendLight: Universal Attention-Based Reinforcement Learning Model for Traffic Signal Control

arXiv: https://arxiv.org/abs/2010.05772 · HTML: https://mlsanity.com/paper/2010.05772/

## Key facts

- Cited by 62 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Oct 2020.

## Built on by (most cited first, 30 of 62)

- [LLMLight: Large Language Models as Traffic Signal Control Agents](https://mlsanity.com/paper/2312.16044/) (2023)
- [LLM-Assisted Light: Leveraging Large Language Model Capabilities for Human-Mimetic Traffic Signal Control in Complex Urban Environments](https://mlsanity.com/paper/2403.08337/) (2024)
- [Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal control](https://mlsanity.com/paper/2112.10107/) (2021)
- [iLLM-TSC: Integration reinforcement learning and large language model for traffic signal control policy improvement](https://mlsanity.com/paper/2407.06025/) (2024)
- [LibSignal: An Open Library for Traffic Signal Control](https://mlsanity.com/paper/2211.10649/) (2022)
- [CoLLMLight: Cooperative Large Language Model Agents for Network-Wide Traffic Signal Control](https://mlsanity.com/paper/2503.11739/) (2025)
- [Reinforcement Learning Approaches for Traffic Signal Control under Missing Data](https://mlsanity.com/paper/2304.10722/) (2023)
- [UniTSA: A Universal Reinforcement Learning Framework for V2X Traffic Signal Control](https://mlsanity.com/paper/2312.05090/) (2023)
- [Multi-Agent Reinforcement Learning for Traffic Signal Control through Universal Communication Method](https://mlsanity.com/paper/2204.12190/) (2022)
- [The Impact of Task Underspecification in Evaluating Deep Reinforcement Learning](https://mlsanity.com/paper/2210.08607/) (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)
- [DuaLight: Enhancing Traffic Signal Control by Leveraging Scenario-Specific and Scenario-Shared Knowledge](https://mlsanity.com/paper/2312.14532/) (2023)
- [MetaVIM: Meta Variationally Intrinsic Motivated Reinforcement Learning for Decentralized Traffic Signal Control](https://mlsanity.com/paper/2101.00746/) (2021)
- [ModelLight: Model-Based Meta-Reinforcement Learning for Traffic Signal Control](https://mlsanity.com/paper/2111.08067/) (2021)
- [DenseLight: Efficient Control for Large-scale Traffic Signals with Dense Feedback](https://mlsanity.com/paper/2306.07553/) (2023)
- [CoSLight: Co-optimizing Collaborator Selection and Decision-making to Enhance Traffic Signal Control](https://mlsanity.com/paper/2405.17152/) (2024)
- [CBLab: Supporting the Training of Large-scale Traffic Control Policies with Scalable Traffic Simulation](https://mlsanity.com/paper/2210.00896/) (2022)
- [SocialLight: Distributed Cooperation Learning towards Network-Wide Traffic Signal Control](https://mlsanity.com/paper/2305.16145/) (2023)
- [Reinforcement learning for traffic signal control in hybrid action space](https://mlsanity.com/paper/2211.12956/) (2022)
- [Improving the generalizability and robustness of large-scale traffic signal control](https://mlsanity.com/paper/2306.01925/) (2023)
- [DiffLight: A Partial Rewards Conditioned Diffusion Model for Traffic Signal Control with Missing Data](https://mlsanity.com/paper/2410.22938/) (2024)
- [Delayed Propagation Transformer: A Universal Computation Engine towards Practical Control in Cyber-Physical Systems](https://mlsanity.com/paper/2110.15926/) (2021)
- [TinyLight: Adaptive Traffic Signal Control on Devices with Extremely Limited Resources](https://mlsanity.com/paper/2205.00427/) (2022)
- [ADLight: A Universal Approach of Traffic Signal Control with Augmented Data Using Reinforcement Learning](https://mlsanity.com/paper/2210.13378/) (2022)
- [HumanLight: Incentivizing Ridesharing via Human-centric Deep Reinforcement Learning in Traffic Signal Control](https://mlsanity.com/paper/2304.03697/) (2023)
- [OffLight: An Offline Multi-Agent Reinforcement Learning Framework for Traffic Signal Control](https://mlsanity.com/paper/2411.06601/) (2024)
- [A Constrained Multi-Agent Reinforcement Learning Approach to Autonomous Traffic Signal Control](https://mlsanity.com/paper/2503.23626/) (2025)
- [VLMLight: Safety-Critical Traffic Signal Control via Vision-Language Meta-Control and Dual-Branch Reasoning Architecture](https://mlsanity.com/paper/2505.19486/) (2025)
- [DataLight: Offline Data-Driven Traffic Signal Control](https://mlsanity.com/paper/2303.10828/) (2023)
- [FuzzyLight: A Robust Two-Stage Fuzzy Approach for Traffic Signal Control Works in Real Cities](https://mlsanity.com/paper/2501.15820/) (2025)

## Abstract (arXiv)

We propose AttendLight, an end-to-end Reinforcement Learning (RL) algorithm for the problem of traffic signal control. Previous approaches for this problem have the shortcoming that they require training for each new intersection with a different structure or traffic flow distribution. AttendLight solves this issue by training a single, universal model for intersections with any number of roads, lanes, phases (possible signals), and traffic flow. To this end, we propose a deep RL model which incorporates two attention models. The first attention model is introduced to handle different numbers of roads-lanes; and the second attention model is intended for enabling decision-making with any number of phases in an intersection. As a result, our proposed model works for any intersection configuration, as long as a similar configuration is represented in the training set. Experiments were conducted with both synthetic and real-world standard benchmark data-sets. The results we show cover intersections with three or four approaching roads; one-directional/bi-directional roads with one, two, and three lanes; different number of phases; and different traffic flows. We consider two regimes: (i) single-environment training, single-deployment, and (ii) multi-environment training, multi-deployment. AttendLight outperforms both classical and other RL-based approaches on all cases in both regimes.