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Robust Policy Optimization via Adversarial Importance Sampling
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...