Value-Decomposed Reinforcement Learning Framework for Taxiway Routing with Hierarchical Conflict-Aware Observations
Learn how to apply a value-decomposed reinforcement learning framework to taxiway routing with hierarchical conflict-aware observations for improved safety and efficiency in airport surface operations
- Implement a value-decomposed reinforcement learning framework using CaTR to optimize taxiway routing
- Design hierarchical conflict-aware observations to capture downstream traffic conflicts
- Train the model using real-time data from airport surface operations
- Test and evaluate the framework's performance in various scenarios
- Apply the framework to real-world airport operations to improve safety and efficiency
This research benefits airport operations teams, including air traffic controllers and airport managers, by providing a more efficient and safe method for taxiway routing and conflict avoidance
💡 A value-decomposed reinforcement learning framework can effectively balance multiple objectives and represent downstream traffic conflicts in taxiway routing
🚀 Improve airport safety and efficiency with a value-decomposed reinforcement learning framework for taxiway routing! 🛬️
Key Takeaways
Learn how to apply a value-decomposed reinforcement learning framework to taxiway routing with hierarchical conflict-aware observations for improved safety and efficiency in airport surface operations
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Abstract:
arXiv:2605.08754v1 Announce Type: new Abstract: Taxiway routing and on-surface conflict avoidance are coupled safety-critical decision problems in airport surface operations. Existing planning and optimization methods are often limited by online computational cost, while reinforcement learning methods may struggle to represent downstream traffic conflicts and balance multiple objectives. This paper presents Conflict-aware Taxiway Routing (CaTR), a reinforcement learning framework for real-time m
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