Summary of Heterogeneous Graph Reinforcement Learning For Dependency-aware Multi-task Allocation in Spatial Crowdsourcing, by Yong Zhao et al.
Heterogeneous Graph Reinforcement Learning for Dependency-aware Multi-task Allocation in Spatial Crowdsourcing
by Yong Zhao, Zhengqiu Zhu, Chen Gao, En Wang, Jincai Huang, Fei-Yue Wang
First submitted to arxiv on: 20 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper addresses the challenge of Dependency-aware Multi-task Allocation (DMA) in Spatial Crowdsourcing (SC) platforms, where tasks are increasingly complex and require collaboration among workers with diverse skills. The authors propose a framework called Heterogeneous Graph Reinforcement Learning-based Task Allocation (HGRL-TA), which leverages a multi-relation graph model and Compound-path-based Heterogeneous Graph Attention Network (CHANet) to represent and capture intricate relations among tasks and workers. A policy network determines the task allocation decision, trained simultaneously with CHANET using the proximal policy optimization algorithm. Experimental results demonstrate the effectiveness of HGRL-TA in solving DMA, yielding average profits 21.78% higher than metaheuristic methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps people work together better on complex tasks online. Right now, these platforms are really good at giving simple jobs to people with different skills. But what if the jobs are really complicated and need many people working together? That’s a big problem! The authors came up with a new way to solve this problem by creating a special model that looks at all the relationships between tasks and workers. This helps make sure the right person is assigned to the right job, even when it’s hard or complex. They tested their idea and found that it worked really well! |
Keywords
» Artificial intelligence » Graph attention network » Multi task » Optimization » Reinforcement learning
