Author

Jamal Bentahar

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#machine learning Preprint Sep 2026

A Unified and Constrained View of Regularization-Based Robust Reinforcement Learning

Regularization-based methods have become a standard approach for training Deep Reinforcement Learning policies against adversarial input perturbations. In this paper, we unify these methods by deriving new upper bounds on the performance gap between the nominal and worst-case policies. Each upper bound is expressed as...

Amine Andam, Jamal Bentahar, M. Hedabou · 0 citations

#machine learning Preprint Sep 2026

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...

Amine Andam, Jamal Bentahar, M. Hedabou · 0 citations