# From Single Agent to Multi-Agent: Improving Traffic Signal Control

arXiv: https://arxiv.org/abs/2406.13693 · HTML: https://mlsanity.com/paper/2406.13693/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 21 references that are in the map.
- First posted on arXiv 19 Jun 2024.

## Builds on (most cited first, 6 of 6)

- [Self-Consistency Improves Chain of Thought Reasoning in Language Models](https://mlsanity.com/paper/2203.11171/) (2022)
- [Improving Factuality and Reasoning in Language Models through Multiagent Debate](https://mlsanity.com/paper/2305.14325/) (2023)
- [Large Language Models](https://mlsanity.com/paper/2307.05782/) (2023)
- [CoLight: Learning Network-level Cooperation for Traffic Signal Control](https://mlsanity.com/paper/1905.05717/) (2019)
- [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)

## Abstract (arXiv)

Due to accelerating urbanization, the importance of solving the signal control problem increases. This paper analyzes various existing methods and suggests options for increasing the number of agents to reduce the average travel time. Experiments were carried out with 2 datasets. The results show that in some cases, the implementation of multiple agents can improve existing methods. For a fine-tuned large language model approach there is small enhancement on all metrics.