---
title: "Credit Assignment and Fine-Tuning Enhanced Reinforcement Learning for Collaborative Spatial Crowdsourcing"
authors:
  - "Wei Chen"
  - "Yafei Li"
  - "Baolong Mei"
  - "Guanglei Zhu"
  - "Jiaqi Wu"
  - "Mingliang Xu"
doi: "10.24963/ijcai.2025/459"
url: "https://ijcai.org/proceedings/2025/459"
pdf: "https://ijcai.org/proceedings/2025/0459.pdf"
venue: "Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence"
year: 2025
pages: "4119-4127"
track: "Main Track"
keywords:
  - "Humans and AI: HAI: Applications"
  - "Agent-based and Multi-agent Systems: MAS: Applications"
  - "Humans and AI: HAI: Human computation and crowdsourcing"
---

# Credit Assignment and Fine-Tuning Enhanced Reinforcement Learning for Collaborative Spatial Crowdsourcing

Wei Chen, Yafei Li, Baolong Mei, Guanglei Zhu, Jiaqi Wu, Mingliang Xu

*Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence*, pages 4119-4127. DOI: [10.24963/ijcai.2025/459](https://doi.org/10.24963/ijcai.2025/459)

## Abstract

Collaborative spatial crowdsourcing leverages distributed workers' collective intelligence to accomplish spatial tasks. A central challenge is to efficiently assign suitable workers to collaborate on these tasks. Although mainstream reinforcement learning (RL) methods have proven effective in task allocation, they face two key obstacles: delayed reward feedback and non-stationary data distributions, both hindering optimal allocation and collaborative efficiency. To address these limitations, we propose CAFE (credit assignment and fine-tuning enhanced), a novel multi-agent RL framework for spatial crowdsourcing. CAFE introduces a credit assignment mechanism that distributes rewards based on workers' contributions and spatiotemporal constraints, coupled with bi-level meta-optimization to jointly optimize credit assignment and RL policy. To handle non-stationary spatial task distributions, CAFE employs an adaptive fine-tuning procedure that efficiently adjusts credit assignment parameters while preserving collaborative knowledge. Experiments on two real-world datasets validate the effectiveness of our framework, demonstrating superior performance in terms of task completion and equitable reward redistribution.