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New OGR-MARL framework enhances USV cooperative pursuit capabilities

By PulseAugur Editorial · [2 sources] ·

Researchers have developed OGR-MARL, a novel framework for multi-agent reinforcement learning designed for cooperative pursuit scenarios involving heterogeneous unmanned surface vehicles (USVs) in constrained port waterways. This framework integrates shared evader belief, role-conditioned option targets, and adaptive rule penalties, enabling MARL algorithms to learn corrective actions rather than starting from scratch. When instantiated with various MARL backbones, OGR-MASAC demonstrated a 75.0% capture rate and superior coordination, with promising generalization capabilities shown through zero-shot transfer to a more complex map. AI

IMPACT Enhances cooperative pursuit capabilities for heterogeneous USVs, potentially improving maritime autonomy and coordination in complex environments.

RANK_REASON The cluster contains a research paper detailing a new framework for multi-agent reinforcement learning.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

New OGR-MARL framework enhances USV cooperative pursuit capabilities

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COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Mao Jiayang, Wang Lanfeng, Peng Zhao-Han ·

    OGR-MARL: Option-Guided Residual Multi-Agent Reinforcement Learning for Heterogeneous USV Cooperative Pursuit in Constrained Port Waterways

    arXiv:2608.12995v1 Announce Type: new Abstract: Heterogeneous USV cooperative pursuit in constrained port waterways requires evader interception under navigation, traffic, and role constraints. This paper proposes OGR-MARL, an option-guided residual multi-agent reinforcement lear…

  2. arXiv cs.MA (Multiagent) TIER_1 English(EN) · Peng Zhao-Han ·

    OGR-MARL: Option-Guided Residual Multi-Agent Reinforcement Learning for Heterogeneous USV Cooperative Pursuit in Constrained Port Waterways

    Heterogeneous USV cooperative pursuit in constrained port waterways requires evader interception under navigation, traffic, and role constraints. This paper proposes OGR-MARL, an option-guided residual multi-agent reinforcement learning framework that is decoupled from a specific…

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