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Summary of Harp: Human-assisted Regrouping with Permutation Invariant Critic For Multi-agent Reinforcement Learning, by Huawen Hu et al.


HARP: Human-Assisted Regrouping with Permutation Invariant Critic for Multi-Agent Reinforcement Learning

by Huawen Hu, Enze Shi, Chenxi Yue, Shuocun Yang, Zihao Wu, Yiwei Li, Tianyang Zhong, Tuo Zhang, Tianming Liu, Shu Zhang

First submitted to arxiv on: 18 Sep 2024

Categories

  • Main: Machine Learning (cs.LG)
  • Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Multiagent Systems (cs.MA)

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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 paper proposes HARP, a multi-agent reinforcement learning framework for group-oriented tasks. Existing human-in-the-loop approaches focus on single-agent tasks and require continuous human involvement, increasing the workload and limiting scalability. HARP integrates automatic agent regrouping with strategic human assistance during deployment, allowing non-experts to offer guidance with minimal intervention. The framework dynamically adjusts agent groupings to optimize task completion and seeks human assistance at deployment, utilizing a Permutation Invariant Group Critic for evaluation and refinement. Our approach leverages limited guidance from non-experts, enhancing performance in multiple collaboration scenarios.
Low GrooveSquid.com (original content) Low Difficulty Summary
The paper introduces HARP, a new way to help machines work together better. Right now, people have to tell these machines what to do all the time, which takes up a lot of their time and makes it hard to scale. The authors created a system that lets humans give guidance only when needed, while the machines figure out how to work together more efficiently. This approach allows non-experts to help with minimal effort, making it more practical for real-world applications.

Keywords

* Artificial intelligence  * Reinforcement learning