# SymLight: Exploring Interpretable and Deployable Symbolic Policies for Traffic Signal Control

arXiv: https://arxiv.org/abs/2511.05790 · HTML: https://mlsanity.com/paper/2511.05790/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods.
- Builds on 17 of its 36 references that are in the map.
- First posted on arXiv 8 Nov 2025.

## Placed in

- Deep reinforcement learning methods / [RL for autonomous driving and traffic](https://mlsanity.com/deeprl/rl-for-autonomous-driving-and-traffic/)

## Builds on (most cited first, 17 of 17)

- [Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding](https://mlsanity.com/paper/1510.00149/) (2015)
- [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://mlsanity.com/paper/2311.05232/) (2023)
- [Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients](https://mlsanity.com/paper/1912.04871/) (2019)
- [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)
- [LLMLight: Large Language Models as Traffic Signal Control Agents](https://mlsanity.com/paper/2312.16044/) (2023)
- [Symbolic Physics Learner: Discovering governing equations via Monte Carlo tree search](https://mlsanity.com/paper/2205.13134/) (2022)
- [Diagnosing Reinforcement Learning for Traffic Signal Control](https://mlsanity.com/paper/1905.04716/) (2019)
- [LLM-Assisted Light: Leveraging Large Language Model Capabilities for Human-Mimetic Traffic Signal Control in Complex Urban Environments](https://mlsanity.com/paper/2403.08337/) (2024)
- [Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal control](https://mlsanity.com/paper/2112.10107/) (2021)
- [Learning an Interpretable Traffic Signal Control Policy](https://mlsanity.com/paper/1912.11023/) (2019)
- [Uncertainty Quantification of Large Language Models through Multi-Dimensional Responses](https://mlsanity.com/paper/2502.16820/) (2025)
- [Position weighted backpressure intersection control for urban networks](https://mlsanity.com/paper/1810.11406/) (2018)
- [TinyLight: Adaptive Traffic Signal Control on Devices with Extremely Limited Resources](https://mlsanity.com/paper/2205.00427/) (2022)
- [Learning Traffic Signal Control via Genetic Programming](https://mlsanity.com/paper/2403.17328/) (2024)
- [GPLight+: A Genetic Programming Method for Learning Symmetric Traffic Signal Control Policy](https://mlsanity.com/paper/2508.16090/) (2025)
- [Generalized Phase Pressure Control Enhanced Reinforcement Learning for Traffic Signal Control](https://mlsanity.com/paper/2503.20205/) (2025)

## Built on by (most cited first, 2 of 2)

- [SignalClaw: LLM-Guided Evolutionary Synthesis of Interpretable Traffic Signal Control Skills](https://mlsanity.com/paper/2604.05535/) (2026)
- [Active Inference for Adaptive Traffic Signal Control in Noisy Nonstationary IoT Environments](https://mlsanity.com/paper/2606.13698/) (2026)

## Abstract (arXiv)

Deep Reinforcement Learning have achieved significant success in automatically devising effective traffic signal control (TSC) policies. Neural policies, however, tend to be over-parameterized and non-transparent, hindering their interpretability and deployability on resource-limited edge devices. This work presents SymLight, a priority function search framework based on Monte Carlo Tree Search (MCTS) for discovering inherently interpretable and deployable symbolic priority functions to serve as the TSC policies. The priority function, in particular, accepts traffic features as input and then outputs a priority for each traffic signal phase, which subsequently directs the phase transition. For effective search, we propose a concise yet expressive priority function representation. This helps mitigate the combinatorial explosion of the action space in MCTS. Additionally, a probabilistic structural rollout strategy is introduced to leverage structural patterns from previously discovered high-quality priority functions, guiding the rollout process. Our experiments on real-world datasets demonstrate SymLight's superior performance across a range of baselines. A key advantage is SymLight's ability to produce interpretable and deployable TSC policies while maintaining excellent performance.