# Beihang KDD 2026 paper proposes MoE-Pointer, using Seq2Seq reinforcement learning to jointly optimize rider-drone relay delivery

- Source: PaperWeekly · 微信公众号
- Published: 2026-10-16T12:14:58.000Z
- RoboHot: https://robohot.news/en/items/sqc3hqa7aprkwj76q70h5t592
- Original: https://mp.weixin.qq.com/s?__biz=MzIwMTc4ODE0Mw==&mid=2247723484&idx=2&sn=4d2df0d39ac5ba457efb0a51660a9651&chksm=97eb7f411647500fd389efc2b62344a27b4fc8f6ab1ec723153368bb73512c980363049919d8&scene=126&sessionid=1792240475#rd

## Summary

Professor Wang Jingyuan's team at Beihang University, together with the School of Management at Shanghai University, published the paper MoE-Pointer at KDD 2026, defining the Dynamic Multi-Stage Pickup and Delivery Problem (DM-PDP) and using a sequence-to-sequence generation framework to achieve end-to-end reinforcement learning joint optimization of a three-stage rider-drone delivery chain.