Abstract:Ride-hailing platforms face the challenge of balancing passenger waiting times with overall system efficiency under highly uncertain supply-demand conditions. Adaptive delayed matching, which controls the holding intervals for batched sets of requests and vehicles, reveals an inherent trade-off between matching and pickup delays. The resulting environment with temporally varying request arrival patterns and dynamic congestion calls for more expressive networks with sufficient capacity to capture their non-stationarity. To address the limitations of existing methods that rely on shallow encoders that cannot capture dynamic supply-demand patterns and congestion effects, we introduce the Regime-Aware Spatio-Temporal Mixture-of-Experts (RAST-MoE) framework, which formalizes adaptive delayed matching as a regime-aware Markov Decision Process and equips RL agents with a self-attention MoE encoder. Instead of relying on a single monolithic network, our design allows different experts to specialize automatically in varying operational conditions, improving representation capacity while maintaining per-sample computation efficiency. Despite its modest size of only 12M parameters, our framework consistently outperforms strong baselines. On real-world Uber trajectory data from San Francisco, it reduces average matching delay by 10%, and pickup delay by 15%. In addition, it demonstrates robustness to unseen demand regimes, stable training behavior without reward hacking, and expert specialization to different regimes. This study shows the strength of MoE-enhanced RL for large-scale decision-making tasks with complex spatiotemporal dynamics.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2512.13727 [cs.LG] |
| (or arXiv:2512.13727v2 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2512.13727 arXiv-issued DOI via DataCite |
|
| Journal reference: | ICML 2026 |
From: Yuhan Tang [view email]
[v1]
Sat, 13 Dec 2025 20:49:15 UTC (8,000 KB)
[v2]
Mon, 4 May 2026 15:30:26 UTC (4,878 KB)
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