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HARL-A: An Extensible Benchmark Framework for Heterogeneous Multi-Agent Adversarial Reinforcement Learning in IsaacLab
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 simplifie
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