# LibSignal: An Open Library for Traffic Signal Control

arXiv: https://arxiv.org/abs/2211.10649 · HTML: https://mlsanity.com/paper/2211.10649/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 35 references that are in the map.
- First posted on arXiv 19 Nov 2022.

## Builds on (most cited first, 15 of 15)

- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [Sim-to-Real Transfer of Robotic Control with Dynamics Randomization](https://mlsanity.com/paper/1710.06537/) (2017)
- [Sim-to-Real Transfer in Deep Reinforcement Learning for Robotics: a Survey](https://mlsanity.com/paper/2009.13303/) (2020)
- [PettingZoo: Gym for Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2009.14471/) (2020)
- [Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal Control](https://mlsanity.com/paper/1903.04527/) (2019)
- [CoLight: Learning Network-level Cooperation for Traffic Signal Control](https://mlsanity.com/paper/1905.05717/) (2019)
- [CityFlow: A Multi-Agent Reinforcement Learning Environment for Large Scale City Traffic Scenario](https://mlsanity.com/paper/1905.05217/) (2019)
- [Learning Phase Competition for Traffic Signal Control](https://mlsanity.com/paper/1905.04722/) (2019)
- [A Survey on Traffic Signal Control Methods](https://mlsanity.com/paper/1904.08117/) (2019)
- [AttendLight: Universal Attention-Based Reinforcement Learning Model for Traffic Signal Control](https://mlsanity.com/paper/2010.05772/) (2020)
- [Diagnosing Reinforcement Learning for Traffic Signal Control](https://mlsanity.com/paper/1905.04716/) (2019)
- [IG-RL: Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal Control](https://mlsanity.com/paper/2003.05738/) (2020)
- [GeneraLight: Improving Environment Generalization of Traffic Signal Control via Meta Reinforcement Learning](https://mlsanity.com/paper/2009.08052/) (2020)
- [DynSTGAT: Dynamic Spatial-Temporal Graph Attention Network for Traffic Signal Control](https://mlsanity.com/paper/2109.05491/) (2021)
- [A Deep Reinforcement Learning Approach for Fair Traffic Signal Control](https://mlsanity.com/paper/2107.10146/) (2021)

## Built on by (most cited first, 16 of 16)

- [Open-TI: Open Traffic Intelligence with Augmented Language Model](https://mlsanity.com/paper/2401.00211/) (2023)
- [eTraM: Event-based Traffic Monitoring Dataset](https://mlsanity.com/paper/2403.19976/) (2024)
- [CoMAL: Collaborative Multi-Agent Large Language Models for Mixed-Autonomy Traffic](https://mlsanity.com/paper/2410.14368/) (2024)
- [SafeLight: A Reinforcement Learning Method toward Collision-free Traffic Signal Control](https://mlsanity.com/paper/2211.10871/) (2022)
- [Large Language Models and Their Applications in Roadway Safety and Mobility Enhancement: A Comprehensive Review](https://mlsanity.com/paper/2506.06301/) (2025)
- [Towards Multi-agent Reinforcement Learning based Traffic Signal Control through Spatio-temporal Hypergraphs](https://mlsanity.com/paper/2404.11014/) (2024)
- [SynTraC: A Synthetic Dataset for Traffic Signal Control from Traffic Monitoring Cameras](https://mlsanity.com/paper/2408.09588/) (2024)
- [Uncertainty-aware Grounded Action Transformation towards Sim-to-Real Transfer for Traffic Signal Control](https://mlsanity.com/paper/2307.12388/) (2023)
- [VLMLight: Safety-Critical Traffic Signal Control via Vision-Language Meta-Control and Dual-Branch Reasoning Architecture](https://mlsanity.com/paper/2505.19486/) (2025)
- [TranSimHub:A Unified Air-Ground Simulation Platform for Multi-Modal Perception and Decision-Making](https://mlsanity.com/paper/2510.15365/) (2025)
- [PyTSC: A Unified Platform for Multi-Agent Reinforcement Learning in Traffic Signal Control](https://mlsanity.com/paper/2410.18202/) (2024)
- [Joint-Local Grounded Action Transformation for Sim-to-Real Transfer in Multi-Agent Traffic Control](https://mlsanity.com/paper/2507.15174/) (2025)
- [CFLight: Enhancing Safety with Traffic Signal Control through Counterfactual Learning](https://mlsanity.com/paper/2512.09368/) (2025)
- [HONEST-CAV: Hierarchical Optimization of Network Signals and Trajectories for Connected and Automated Vehicles with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2602.18740/) (2026)
- [OracleTSC: Oracle-Informed Reward Hurdle and Uncertainty Regularization for Traffic Signal Control](https://mlsanity.com/paper/2605.08516/) (2026)
- [Preference-Conditioned Multi-Objective Reinforcement Learning for Runtime-Tunable Transit Signal Priority](https://mlsanity.com/paper/2607.18286/) (2026)

## Abstract (arXiv)

This paper introduces a library for cross-simulator comparison of reinforcement learning models in traffic signal control tasks. This library is developed to implement recent state-of-the-art reinforcement learning models with extensible interfaces and unified cross-simulator evaluation metrics. It supports commonly-used simulators in traffic signal control tasks, including Simulation of Urban MObility(SUMO) and CityFlow, and multiple benchmark datasets for fair comparisons. We conducted experiments to validate our implementation of the models and to calibrate the simulators so that the experiments from one simulator could be referential to the other. Based on the validated models and calibrated environments, this paper compares and reports the performance of current state-of-the-art RL algorithms across different datasets and simulators. This is the first time that these methods have been compared fairly under the same datasets with different simulators.