# A Robust and Efficient Multi-Agent Reinforcement Learning Framework for Traffic Signal Control

arXiv: https://arxiv.org/abs/2603.12096 · HTML: https://mlsanity.com/paper/2603.12096/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 23 references that are in the map.
- First posted on arXiv 12 Mar 2026.

## Builds on (most cited first, 9 of 9)

- [The Surprising Effectiveness of PPO in Cooperative, Multi-Agent Games](https://mlsanity.com/paper/2103.01955/) (2021)
- [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)
- [AttendLight: Universal Attention-Based Reinforcement Learning Model for Traffic Signal Control](https://mlsanity.com/paper/2010.05772/) (2020)
- [DRLE: Decentralized Reinforcement Learning at the Edge for Traffic Light Control in the IoV](https://mlsanity.com/paper/2009.01502/) (2020)
- [Traffic Signal Cycle Control with Centralized Critic and Decentralized Actors under Varying Intervention Frequencies](https://mlsanity.com/paper/2406.08248/) (2024)
- [Reinforcement Learning for Traffic Signal Control: Comparison with Commercial Systems](https://mlsanity.com/paper/2104.10455/) (2021)
- [Phase Re-service in Reinforcement Learning Traffic Signal Control](https://mlsanity.com/paper/2407.14775/) (2024)
- [VissimRL: A Multi-Agent Reinforcement Learning Framework for Traffic Signal Control Based on Vissim](https://mlsanity.com/paper/2601.18284/) (2026)

## Abstract (arXiv)

Reinforcement Learning (RL) in Traffic Signal Control (TSC) faces significant hurdles in real-world deployment due to limited generalization to dynamic traffic flow variations. Existing approaches often overfit static patterns and use action spaces incompatible with driver expectations. This paper proposes a robust Multi-Agent Reinforcement Learning (MARL) framework validated in the Vissim traffic simulator. The framework integrates three mechanisms: (1) Turning Ratio Randomization, a training strategy that exposes agents to dynamic turning probabilities to enhance robustness against unseen scenarios; (2) a stability-oriented Exponential Phase Duration Adjustment action space, which balances responsiveness and precision through cyclical, exponential phase adjustments; and (3) a Neighbor-Based Observation scheme utilizing the MAPPO algorithm with Centralized Training with Decentralized Execution (CTDE). By leveraging centralized updates, this approach approximates the efficacy of global observations while maintaining scalable local communication. Experimental results demonstrate that our framework outperforms standard RL baselines, reducing average waiting time by over 10%. The proposed model exhibits superior generalization in unseen traffic scenarios and maintains high control stability, offering a practical solution for adaptive signal control.