RAST-MoE-RL: Advancing Ride-Hailing with Regime-Aware Spatio-Temporal Reinforcement Learning

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.

• •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.