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

TL;DR

HARP is a multi-agent reinforcement learning framework that combines automatic regrouping with minimal human assistance, enabling non-experts to effectively guide multi-agent collaboration and improve task performance.

Contribution

HARP introduces a novel permutation invariant critic and dynamic regrouping mechanism for multi-agent RL, reducing human workload and enhancing scalability.

Findings

Effective in multiple collaboration scenarios

Leverages limited human guidance to improve performance

Allows non-experts to contribute valuable suggestions

Abstract

Human-in-the-loop reinforcement learning integrates human expertise to accelerate agent learning and provide critical guidance and feedback in complex fields. However, many existing approaches focus on single-agent tasks and require continuous human involvement during the training process, significantly increasing the human workload and limiting scalability. In this paper, we propose HARP (Human-Assisted Regrouping with Permutation Invariant Critic), a multi-agent reinforcement learning framework designed for group-oriented tasks. HARP integrates automatic agent regrouping with strategic human assistance during deployment, enabling and allowing non-experts to offer effective guidance with minimal intervention. During training, agents dynamically adjust their groupings to optimize collaborative task completion. When deployed, they actively seek human assistance and utilize the Permutation Invariant Group Critic to evaluate and refine human-proposed groupings, allowing non-expert users to contribute valuable suggestions. In multiple collaboration scenarios, our approach is able to leverage limited guidance from non-experts and enhance performance. The project can be found at https://github.com/huawen-hu/HARP.