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Robust Adversarial Reinforcement Learning with Risk Sensitivity and Critic Consistency Regularization

arXiv · AI, language, vision and robotics · article · Sep 23, 2026 · UTC

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 from unstable optimization and degraded value estimation. In particular, overly aggressive adversaries can drive the agent toward uninformative failure states, while adversarial perturbations amplify disagreement between double critics and introduce biased value targets. We propose a unified framewo

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