# Enhancing Traffic Signal Control through Model-based Reinforcement Learning and Policy Reuse

arXiv: https://arxiv.org/abs/2503.08728 · HTML: https://mlsanity.com/paper/2503.08728/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Multi-agent reinforcement learning, one of 15 directions in Deep reinforcement learning methods.
- Builds on 15 of its 29 references that are in the map.
- First posted on arXiv 11 Mar 2025.

## Placed in

- Deep reinforcement learning methods / [Multi-agent reinforcement learning](https://mlsanity.com/deeprl/multi-agent-reinforcement-learning/)

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

- [Transfer Learning in Deep Reinforcement Learning: A Survey](https://mlsanity.com/paper/2009.07888/) (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)
- [Deep Reinforcement Learning for Intelligent Transportation Systems: A Survey](https://mlsanity.com/paper/2005.00935/) (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)
- [Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal control](https://mlsanity.com/paper/2112.10107/) (2021)
- [Multi-Agent MDP Homomorphic Networks](https://mlsanity.com/paper/2110.04495/) (2021)
- [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)
- [SafeLight: A Reinforcement Learning Method toward Collision-free Traffic Signal Control](https://mlsanity.com/paper/2211.10871/) (2022)
- [An Optimal Online Method of Selecting Source Policies for Reinforcement Learning](https://mlsanity.com/paper/1709.08201/) (2017)
- [EMVLight: a Multi-agent Reinforcement Learning Framework for an Emergency Vehicle Decentralized Routing and Traffic Signal Control System](https://mlsanity.com/paper/2206.13441/) (2022)
- [Leveraging Queue Length and Attention Mechanisms for Enhanced Traffic Signal Control Optimization](https://mlsanity.com/paper/2201.00006/) (2021)
- [Model-Augmented Q-learning](https://mlsanity.com/paper/2102.03866/) (2021)

## Abstract (arXiv)

Multi-agent reinforcement learning (MARL) has shown significant potential in traffic signal control (TSC). However, current MARL-based methods often suffer from insufficient generalization due to the fixed traffic patterns and road network conditions used during training. This limitation results in poor adaptability to new traffic scenarios, leading to high retraining costs and complex deployment. To address this challenge, we propose two algorithms: PLight and PRLight. PLight employs a model-based reinforcement learning approach, pretraining control policies and environment models using predefined source-domain traffic scenarios. The environment model predicts the state transitions, which facilitates the comparison of environmental features. PRLight further enhances adaptability by adaptively selecting pre-trained PLight agents based on the similarity between the source and target domains to accelerate the learning process in the target domain. We evaluated the algorithms through two transfer settings: (1) adaptability to different traffic scenarios within the same road network, and (2) generalization across different road networks. The results show that PRLight significantly reduces the adaptation time compared to learning from scratch in new TSC scenarios, achieving optimal performance using similarities between available and target scenarios.