Explainable Reinforcement Learning for Adaptive Traffic Signal Control
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 intersectio...
| Authors | Dickens Kwesiga, Nishu Choudhary, Angshuman Guin et al. |
|---|---|
| Primary category | cs.AI |
| Published | 4 July 2026 |
| Last updated at source | 4 July 2026 |
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