Partially Observable Learning for Multi-Platform Dispatch Optimization

2026-08-11

AI Summary

This work addresses the challenge of collaborative dispatch in multi-platform instant delivery systems, where inter-platform privacy constraints restrict global observability and severely degrade the performance of existing methods. To overcome this limitation, the authors propose POLO, a novel framework that models each platform–grid pair as an independent agent relying solely on local observations. POLO aggregates courier information via an attention mechanism and incorporates counterfactual reward shaping to enhance learning stability. The approach enables scalable coordination while preserving platform-level privacy. Extensive experiments demonstrate that POLO consistently outperforms strong baselines across varying numbers of platforms and system scales, achieving superior robustness and effectiveness in both platform revenue and courier trip efficiency.

Abstract

Instant delivery platforms have become a critical component of urban logistics, increasingly relying on crowdsourced couriers to fulfill highly dynamic orders. In real-world systems, couriers are not exclusive to a single platform and may concurrently serve multiple platforms, while each platform can only observe its own orders and couriers' interactions due to privacy and operational constraints. This results in a multi-platform dispatch environment with inherent partial observability. However, most existing works on dispatch optimization assume full courier observability and mandatory assignment acceptance, causing substantial performance degradation when deployed in realistic multi-platform settings. In this paper, we propose POLO, a partially observable multi-agent reinforcement learning framework for dispatching optimization in multi-platform instant delivery systems. POLO firstly models each platform-grid pair as an independent agent that learns dispatch policies solely from platform-local observations, aligning the learning process with real-world privacy and operational constraints. To support effective decision-making under incomplete and heterogeneous courier information, POLO introduces a novel attention-based policy representation that selectively aggregates inter-courier information. Moreover, we design a counterfactual reward shaping mechanism to mitigate the non-stationarity induced by joint actions across grids, leading to more stable and scalable learning. We develop a high-fidelity simulator to evaluate dispatch performance under varying numbers of platforms and system scales. Extensive experiments demonstrate that POLO consistently outperforms strong baselines in terms of platform revenue and courier travel efficiency, highlighting its robustness and effectiveness in realistic multi-platform settings.

Problem

• partially observable
• multi-platform dispatch
• crowdsourced couriers
• instant delivery
• privacy constraints

Innovation

• partially observable multi-agent reinforcement learning
• attention-based policy representation
• counterfactual reward shaping
• multi-platform dispatch optimization
• urban logistics