IsaacLab中可扩展异构多智能体对抗强化学习框架
HARL-A: An Extensible Benchmark Framework for Heterogeneous Multi-Agent Adversarial Reinforcement Learning in IsaacLab
AI总结：
针对现有MARL研究多聚焦协作、对抗场景研究不足的问题，研究扩展IsaacLab框架，集成竞争性HAPPO算法，构建含异构非对称智能体的对抗环境，实现高保真仿真下的高效鲁棒对抗策略训练。
摘要：
多智能体强化学习（MARL）是动态环境中协作机器人系统的核心技术。现有研究多聚焦于这类协作场景，而对抗交互在追逃、安防、竞争性操作等现实应用中同样至关重要。本研究扩展了IsaacLab框架，使其支持在高保真物理仿真中可扩展地训练对抗策略。我们提出了一套对抗MARL环境，包含具有非对称目标与能力的异构智能体。该平台集成了异构智能体强化学习近端策略优化（HAPPO）的竞争性变体，可在对抗动态下实现高效训练与评估。在多个基准场景下的实验表明，该框架能够为形态多样的多智能体竞争建模并训练鲁棒策略，同时保持高吞吐量与仿真真实度。代码与基准可访问：https://github.com/DIRECTLab/IsaacLab-HARL 。
英文摘要：
Progress in adversarial multi-agent reinforcement learning (MARL) for robotics has been hampered by a lack of shared, extensible infrastructure that supports heterogeneous agent morphologies in high-fidelity physics simulation. Existing frameworks either focus on cooperative tasks, rely on simplified physics engines, or provide isolated implementations that are difficult to extend. We present HARL-A, an open-source, actively maintained framework built on IsaacLab that enables scalable training and benchmarking of adversarial policies across morphologically diverse robot teams with any number of teams and any mix of robot morphologies per team. HARL-A extends the HARL algorithm library and IsaacLab with adversarial multi-agent support and contributes three components: (1) a modular software architecture that reduces the engineering overhead of defining new heterogeneous adversarial environments, (2) a suite of three benchmark environments---Sumo, Soccer, and 3D Galaga---spanning contact-rich pushing, ball-skill competition, and pursuit/evasion, (3) over ten pretrained policies spanning homogeneous and heterogeneous team configurations, released publicly on Hugging Face to enable immediate exploration of adversarial learning dynamics without retraining from scratch. We demonstrate the framework across multiple competitive scenarios, showing that it reliably produces learned adversarial policies and emergent role specialization. All code environments, trained policies, and documentation are openly available at https://github.com/DIRECTLab/IsaacLab-HARL.
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