Hugging Face Trending Papers June 2, 2026

Post-Hoc Robustness for Model-Based Reinforcement Learning

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To improve the real-world applicability of reinforcement learning (RL), the field of adversarially robust RL studies how to train agents under adversarial environment perturbations. In this setting, a protagonist agent optimizes a policy under environmental perturbations from an adversary, resulting in a zero-sum Markov game.

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