M²GRPO: 生体模倣水中ロボットの追跡のためのMambaベースのマルチエージェントグループ相対方策最適化
M$^{2}$GRPO: Mamba-based Multi-Agent Group Relative Policy Optimization for Biomimetic Underwater Robots Pursuit
生体模倣水中ロボットの協調追跡問題に対し、Mambaポリシーとグループ相対方策最適化を組み合わせた新しいフレームワークM²GRPOを提案し、シミュレーションと実機実験で既存手法より高い追跡成功率と捕獲効率を達成した。
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著者: Yukai Feng, Zhiheng Wu, Zhengxing Wu, Junwen Gu, Junzhi Yu
分類: cs.RO, cs.AI
原文アブストラクト
Traditional policy learning methods in cooperative pursuit face fundamental challenges in biomimetic underwater robots, where long-horizon decision making, partial observability, and inter-robot coordination require both expressiveness and stability. To address these issues, a novel framework called Mamba-based multi-agent group relative policy optimization (M$^{2}$GRPO) is proposed, which integrates a selective state-space Mamba policy with group-relative policy optimization under the centralized-training and decentralized-execution (CTDE) paradigm. Specifically, the Mamba-based policy leverages observation history to capture long-horizon temporal dependencies and exploits attention-based relational features to encode inter-agent interactions, producing bounded continuous actions through normalized Gaussian sampling. To further improve credit assignment without sacrificing stability, the group-relative advantages are obtained by normalizing rewards across agents within each episode and optimized through a multi-agent extension of GRPO, significantly reducing the demand for training resources while enabling stable and scalable policy updates. Extensive simulations and real-world pool experiments across team scales and evader strategies demonstrate that M$^{2}$GRPO consistently outperforms MAPPO and recurrent baselines in both pursuit success rate and capture efficiency. Overall, the proposed framework provides a practical and scalable solution for cooperative underwater pursuit with biomimetic robot systems.
