SIGMA: Symmetry-aware, Intelligent, Geometric, Multi-objective Adaptive Control for Robust, Dependable Traffic Management
SIGMA is a reinforcement learning framework for traffic signal control that integrates a large language model to convert natural-language emergency commands into priority vectors, eliminating manual reward engineering The system employs rotational augmentation for orientation-invariant learning, enabling transferability across geometrically similar four-way intersections Evaluated in SUMO on four Kolkata-based urban intersections, SIGMA outperforms fixed-time, actuated, and DQN controllers by re
Analysis
TL;DR
- SIGMA is a reinforcement learning framework for traffic signal control that integrates a large language model to convert natural-language emergency commands into priority vectors, eliminating manual reward engineering
- The system employs rotational augmentation for orientation-invariant learning, enabling transferability across geometrically similar four-way intersections
- Evaluated in SUMO on four Kolkata-based urban intersections, SIGMA outperforms fixed-time, actuated, and DQN controllers by reducing average/emergency waiting times, queue lengths, and boosting throughput
- The framework defines and validates reliability properties including emergency service levels, graceful degradation under LLM failures, and demand sensitivity using bootstrap statistics
- Ablation studies confirm robustness to both component failures and geometric rotations, demonstrating practical deployability
Why It Matters
This work represents a significant step toward language-guided, multi-objective reinforcement learning systems for real-world urban infrastructure, bridging the gap between NLP capabilities and control theory. For AI practitioners, it demonstrates a novel architecture where LLMs serve as adaptive objective tuners rather than mere interfaces, offering a template for integrating semantic understanding into safety-critical decision-making systems. The emphasis on statistical reliability assurance and graceful degradation under component failures addresses a critical gap in deploying RL-based control systems in production environments.
Technical Details
- Architecture: Multi-objective actor-critic controller enhanced with an LLM module that translates natural-language emergency commands (e.g., ambulance priority) into numerical priority vectors, enabling dynamic objective tuning without manual reward design
- Symmetry-aware learning: Rotational augmentation is applied during training to ensure orientation-invariant policy learning, allowing the controller to generalize across intersections with different geometric alignments
- Offline-to-online learning: The system uses offline pretraining for stable initialization, followed by gradual online adaptation to changing traffic demand patterns, balancing sample efficiency with real-time responsiveness
- Reliability framework: Formal reliability properties are defined covering emergency service levels, graceful degradation when the LLM component fails, and demand sensitivity; these are validated using bootstrap statistical methods
- Evaluation: Tested in SUMO simulation on four real-world Kolkata intersections against baselines including fixed-time, actuated, and DQN controllers, with ablation studies isolating the contribution of each component
Industry Insight
- The integration of LLMs as adaptive objective tuners in RL control loops offers a scalable pattern for other safety-critical domains (e.g., energy grid management, healthcare resource allocation) where objectives shift dynamically and manual reward engineering is prohibitive
- The emphasis on statistical reliability assurance and graceful degradation under component failures should become a standard requirement for any RL system targeting real-world deployment, shifting the field from pure performance optimization to dependability-aware design
- Rotational and geometric augmentation for transferability across similar environments could accelerate the deployment of RL-based urban infrastructure controllers by reducing the per-location data and tuning requirements
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