mlsanityData to 14 Oct 2026

Explainable Reinforcement Learning for Adaptive Traffic Signal Control

arXiv 2607.03703 ↗First posted 4 Jul 2026cs.AI cs.LGcited by 0

On the map: Deep reinforcement learning methods → RL for autonomous driving and traffic

Abstract (arXiv)

Reinforcement Learning (RL) has emerged as a powerful paradigm for adaptive traffic signal control. However, in safety-critical infrastructure like traffic control, the opaque, black-box nature of deep RL models poses challenges for transportation agency acceptance, regulatory compliance, operational trust, troubleshooting, and fine-tuning. To bridge this gap between high-performance optimization and human-comprehensible interpretability, this effort introduces a novel, explainable entity centric RL framework for safe and transparent traffic signal control. Rather than processing traffic states through monolithic, flat vectors, the proposed architecture disaggregates real-time intersection observations into distinct, high-dimensional lane entities and phase temporal configurations to inherently preserve the structural topology and geometric configurations of the intersection. Relational dependencies and inter-lane conflicts are dynamically extracted via a dual-stage attention network featuring sequential multi-head cross-attention and self-attention blocks. This design yields a real time affinity matrix that quantifies the direct influence of signal phases on specific approach volumes and queues, providing full visual and analytical interpretability. To ensure strict operational reliability, a deterministic action-masking interface is integrated directly into the Proximal Policy Optimization pipeline, explicitly blocking invalid phase transitions to guarantee absolute compliance with established signal timing and safety constraints. Evaluated in a microscopic simulation environment, outperforms state-of-the-art baselines in delay minimization. More importantly, the emergent attention weights align precisely with established traffic engineering principles, offering an auditable, trust-enabling, and deployable architecture for next-generation adaptive traffic control systems.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Placed in RL for autonomous driving and traffic, one of 15 directions in Deep reinforcement learning methods. Builds on 22 of its 51 references that are in the map. First posted on arXiv 4 Jul 2026.

Builds on

22 of 51 references.

Closest ancestors · 2

Same direction, newest first

  1. 2024Multi-hop Upstream Anticipatory Traffic Signal Control with Deep Reinforcement Learning10 Nov
  2. 2024Adaptive Transit Signal Priority based on Deep Reinforcement Learning and Connected Vehicles in a Traffic Microsimulation Environment31 Jul

Other references · 20

  1. 2017Attention Is All You Needcited by 60,361
  2. 2015Deep Residual Learning for Image Recognitioncited by 52,096
  3. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  4. 2017Proximal Policy Optimization Algorithmscited by 15,215
  5. 2016Layer Normalizationcited by 5,669
  6. 2014Neural Machine Translation by Jointly Learning to Align and Translatecited by 4,548
  7. 2015Show, Attend and Tell: Neural Image Caption Generation with Visual Attentioncited by 1,423
  8. 2016RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanismcited by 349
  9. 2018Explainable Prediction of Medical Codes from Clinical Textcited by 234
  10. 2019Multi-Agent Deep Reinforcement Learning for Large-scale Traffic Signal Controlcited by 164
  11. 2019CoLight: Learning Network-level Cooperation for Traffic Signal Controlcited by 138
  12. 2019Towards Interpretable Reinforcement Learning Using Attention Augmented Agentscited by 82
8 more
  1. 2018Deep Reinforcement Learning for Traffic Light Control in Vehicular Networkscited by 52
  2. 2017Deep Deterministic Policy Gradient for Urban Traffic Light Controlcited by 30
  3. 2023Multi-Agent Reinforcement Learning Based on Representational Communication for Large-Scale Traffic Signal Controlcited by 7
  4. 2022Model-based graph reinforcement learning for inductive traffic signal controlcited by 5
  5. 2023Improving the generalizability and robustness of large-scale traffic signal controlcited by 5
  6. 2023Deep Reinforcement Learning for Traffic Light Control in Intelligent Transportation Systemscited by 3
  7. 2024Multi-agent Reinforcement Traffic Signal Control based on Interpretable Influence Mechanism and Biased ReLU Approximationcited by 2
  8. 2024Integrating Transit Signal Priority into Multi-Agent Reinforcement Learning based Traffic Signal Controlcited by 2

Built on by

Not cited yet.

Similar, not cited

Close in wording, no citation either way

  1. 2023A Fully Data-Driven Approach for Realistic Traffic Signal Control Using Offline Reinforcement Learning
  2. 2026Evaluating the Robustness of Reinforcement Learning based Adaptive Traffic Signal Control
  3. 2025Traffic-R1: Reinforced LLMs Bring Human-Like Reasoning to Traffic Signal Control Systems
  4. 2025A Hierarchical Deep Reinforcement Learning Framework for Traffic Signal Control with Predictable Cycle Planning
  5. 2024Reinforcement Learning for Adaptive Traffic Signal Control: Turn-Based and Time-Based Approaches to Reduce Congestion
  6. 2026LATS: Large Language Model Assisted Teacher-Student Framework for Multi-Agent Reinforcement Learning in Traffic Signal Control
  7. 2025Toward Dependency Dynamics in Multi-Agent Reinforcement Learning for Traffic Signal Control
  8. 2026CROSS: A Mixture-of-Experts Reinforcement Learning Framework for Generalizable Large-Scale Traffic Signal Control