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Robust Adversarial Reinforcement Learning with Risk Sensitivity and Critic Consistency Regularization
Reinforcement learning (RL) achieves strong performance in sequential decision-making but remains brittle under dynamic uncertainty and distributional shifts. Robust Adversarial Reinforcement Learning (RARL) improves robustness via worst-case perturbations, but existing approaches frequently suffer
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