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This paper unifies regularization-based methods for training Deep Reinforcement Learning policies against adversarial input perturbations by deriving new upper bounds on the performance gap between the nominal and worst-case policies.
This work introduces Adversarial Importance Sampling (Advis), a method that uses importance sampling over trajectories from standard training to estimate and optimize verifiable worst-case returns and introduces advrl, a modular PyTorch library that provides clean, single-file implementations of existing robustness met...
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