RAST-MoE-RL: Advancing Ride-Hailing with Regime-Aware Spatio-Temporal Reinforcement Learning
Published:Dec 13, 2025 20:49
•1 min read
•ArXiv
Analysis
The research introduces a novel framework, RAST-MoE-RL, to address the complexities of ride-hailing optimization using deep reinforcement learning. This approach likely aims to improve efficiency and responsiveness within a dynamic transportation environment.
Key Takeaways
- •The core innovation is a Regime-Aware Spatio-Temporal Mixture of Experts (MoE) framework.
- •The research focuses on applying deep reinforcement learning to the ride-hailing domain.
- •The paper aims to improve performance within the complex ride-hailing environment.
Reference
“The article is sourced from ArXiv, indicating peer review might not yet be complete.”