WMAttack automates finite-budget attack search for world-model agents via SCAS and RGAR, reporting higher normalized reward drops than baselines on Atari and DMC tasks.
Towards robust model-based reinforce- ment learning against adversarial corruption.arXiv preprint arXiv:2402.08991, 2024
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Wolfpack attack framework disrupts MARL cooperation by targeting initial and assisting agents; WALL trains robust policies against it with reported experimental gains.
citing papers explorer
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WMAttack: Automated Attack Search for Adversarial Evaluation of World-Model Agents
WMAttack automates finite-budget attack search for world-model agents via SCAS and RGAR, reporting higher normalized reward drops than baselines on Atari and DMC tasks.
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Wolfpack Adversarial Attack for Robust Multi-Agent Reinforcement Learning
Wolfpack attack framework disrupts MARL cooperation by targeting initial and assisting agents; WALL trains robust policies against it with reported experimental gains.