{"type":"ScholarlyArticle","title":"Credit Assignment and Fine-Tuning Enhanced Reinforcement Learning for Collaborative Spatial Crowdsourcing","authors":[{"name":"Wei Chen","slug":"wei-chen","given":"Wei","family":"Chen","affiliation":""},{"name":"Yafei Li","slug":"yafei-li","given":"Yafei","family":"Li","affiliation":""},{"name":"Baolong Mei","slug":"baolong-mei","given":"Baolong","family":"Mei","affiliation":""},{"name":"Guanglei Zhu","slug":"guanglei-zhu","given":"Guanglei","family":"Zhu","affiliation":""},{"name":"Jiaqi Wu","slug":"jiaqi-wu","given":"Jiaqi","family":"Wu","affiliation":""},{"name":"Mingliang Xu","slug":"mingliang-xu","given":"Mingliang","family":"Xu","affiliation":""}],"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.","doi":"10.24963/ijcai.2025/459","url":"https://ijcai.org/proceedings/2025/459","pdf":"https://ijcai.org/proceedings/2025/0459.pdf","markdown":"https://ijcai.org/proceedings/2025/459.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/459","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Humans and AI","track":"Main Track","number":459,"pages":{"first":4119,"last":4127},"keywords":["Humans and AI: HAI: Applications","Agent-based and Multi-agent Systems: MAS: Applications","Humans and AI: HAI: Human computation and crowdsourcing"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}