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arXiv Machine Learning By Amine Andam, Jamal Bentahar, Mustapha Hedabou

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

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The paper presents a unified framework for regularization-based robust reinforcement learning by deriving upper bounds on the performance gap between nominal and worst-case policies. These bounds are expressed as a regularization objective plus a KL-divergence penalty, explaining why KL penalties enhance robustness. The authors reformulate robust training as a constrained optimization problem, updating the Lagrange multiplier jointly with the policy to automatically tune regularization, and validate the approach with extensive adversarial evaluations on continuous control tasks.

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