Summary of A Two-stage Reinforcement Learning-based Approach For Multi-entity Task Allocation, by Aicheng Gong et al.
A Two-stage Reinforcement Learning-based Approach for Multi-entity Task Allocation
by Aicheng Gong, Kai Yang, Jiafei Lyu, Xiu Li
First submitted to arxiv on: 29 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 The proposed two-stage task allocation algorithm, based on similarity and utilizing reinforcement learning, addresses the challenges of dynamic task allocation in modern applications such as multi-robot cooperation and resource scheduling. The algorithm utilizes a pre-assign strategy to allow entities to preselect appropriate tasks, avoiding local optima and finding the optimal allocation. Additionally, an attention mechanism and hyperparameter network structure are introduced to adapt to changing numbers and attributes of entities and tasks, enabling generalization to new tasks. Experimental results demonstrate that the algorithm effectively solves dynamic allocation problems, outperforming heuristic algorithms like genetic algorithms, with good zero-shot generalization performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a new way to assign tasks to things (like robots) based on how similar they are. This helps when things change quickly and you need to adjust the assignments. The algorithm is split into two parts: one that lets the things choose their own tasks, and another that adapts to changes in the number of things and tasks. Tests showed that this new approach works better than other methods, especially in situations where things are always changing. |
Keywords
» Artificial intelligence » Attention » Generalization » Hyperparameter » Reinforcement learning » Zero shot
