M²GRPO uses a Mamba-based policy and normalized group-relative advantages under CTDE to achieve higher pursuit success and capture efficiency than MAPPO and recurrent baselines in simulations and pool tests.
Cooperative pursuit policy for bionic underwater robot based on MARL-MHSA architecture: Data-driven modeling and distributed strategy optimization,
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M$^{2}$GRPO: Mamba-based Multi-Agent Group Relative Policy Optimization for Biomimetic Underwater Robots Pursuit
M²GRPO uses a Mamba-based policy and normalized group-relative advantages under CTDE to achieve higher pursuit success and capture efficiency than MAPPO and recurrent baselines in simulations and pool tests.