# IG-RL: Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal Control

arXiv: https://arxiv.org/abs/2003.05738 · HTML: https://mlsanity.com/paper/2003.05738/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2020.

## Built on by (most cited first, 22 of 22)

- [Challenges and Opportunities in Deep Reinforcement Learning with Graph Neural Networks: A Comprehensive review of Algorithms and Applications](https://mlsanity.com/paper/2206.07922/) (2022)
- [Reinforcement learning on graphs: A survey](https://mlsanity.com/paper/2204.06127/) (2022)
- [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)
- [Multi-Agent Reinforcement Learning Based on Representational Communication for Large-Scale Traffic Signal Control](https://mlsanity.com/paper/2310.02435/) (2023)
- [Large-Scale Traffic Signal Control Using Constrained Network Partition and Adaptive Deep Reinforcement Learning](https://mlsanity.com/paper/2303.11899/) (2023)
- [Model-based graph reinforcement learning for inductive traffic signal control](https://mlsanity.com/paper/2208.00659/) (2022)
- [Improving the generalizability and robustness of large-scale traffic signal control](https://mlsanity.com/paper/2306.01925/) (2023)
- [Deep Reinforcement Learning for Traffic Light Control in Intelligent Transportation Systems](https://mlsanity.com/paper/2302.03669/) (2023)
- [A Bibliometric Analysis and Review on Reinforcement Learning for Transportation Applications](https://mlsanity.com/paper/2210.14524/) (2022)
- [A Critical Review of Traffic Signal Control and A Novel Unified View of Reinforcement Learning and Model Predictive Control Approaches for Adaptive Traffic Signal Control](https://mlsanity.com/paper/2211.14426/) (2022)
- [A Fully Data-Driven Approach for Realistic Traffic Signal Control Using Offline Reinforcement Learning](https://mlsanity.com/paper/2311.15920/) (2023)
- [LATS: Large Language Model Assisted Teacher-Student Framework for Multi-Agent Reinforcement Learning in Traffic Signal Control](https://mlsanity.com/paper/2603.24361/) (2026)
- [Scalable Traffic Signal Controls using Fog-Cloud Based Multiagent Reinforcement Learning](https://mlsanity.com/paper/2110.05564/) (2021)
- [DVS-RG: Differential Variable Speed Limits Control using Deep Reinforcement Learning with Graph State Representation](https://mlsanity.com/paper/2405.09163/) (2024)
- [Using a single actor to output personalized policy for different intersections](https://mlsanity.com/paper/2503.07678/) (2025)
- [Enhancing Traffic Signal Control through Model-based Reinforcement Learning and Policy Reuse](https://mlsanity.com/paper/2503.08728/) (2025)
- [Smart Traffic Signals: Comparing MARL and Fixed-Time Strategies](https://mlsanity.com/paper/2505.14544/) (2025)
- [Topology-Assisted Spatio-Temporal Pattern Disentangling for Scalable MARL in Large-scale Autonomous Traffic Control](https://mlsanity.com/paper/2506.12453/) (2025)
- [Reinforcement Learning-Based Traffic Signal Control for IoT-Enabled Intersections](https://mlsanity.com/paper/2606.22108/) (2026)
- [VLALight: A Vision-Language-Action Model for Traffic Signal Control](https://mlsanity.com/paper/2609.36934/) (2026)
- [Continual Graph Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2610.10302/) (2026)

## Abstract (arXiv)

Scaling adaptive traffic-signal control involves dealing with combinatorial state and action spaces. Multi-agent reinforcement learning attempts to address this challenge by distributing control to specialized agents. However, specialization hinders generalization and transferability, and the computational graphs underlying neural-networks architectures -- dominating in the multi-agent setting -- do not offer the flexibility to handle an arbitrary number of entities which changes both between road networks, and over time as vehicles traverse the network. We introduce Inductive Graph Reinforcement Learning (IG-RL) based on graph-convolutional networks which adapts to the structure of any road network, to learn detailed representations of traffic-controllers and their surroundings. Our decentralized approach enables learning of a transferable-adaptive-traffic-signal-control policy. After being trained on an arbitrary set of road networks, our model can generalize to new road networks, traffic distributions, and traffic regimes, with no additional training and a constant number of parameters, enabling greater scalability compared to prior methods. Furthermore, our approach can exploit the granularity of available data by capturing the (dynamic) demand at both the lane and the vehicle levels. The proposed method is tested on both road networks and traffic settings never experienced during training. We compare IG-RL to multi-agent reinforcement learning and domain-specific baselines. In both synthetic road networks and in a larger experiment involving the control of the 3,971 traffic signals of Manhattan, we show that different instantiations of IG-RL outperform baselines.