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cs.LG2026
A Unified and Constrained View of Regularization-Based Robust Reinforcement Learning
Amine Andam, Jamal Bentahar, Mustapha Hedabou
Regularization-based methods have become a standard approach for training Deep Reinforcement Learning policies against adversarial input perturbations. In this paper, we unify thes…
cs.LG2026
Robust Policy Optimization via Adversarial Importance Sampling
Amine Andam, Jamal Bentahar, Mustapha Hedabou
Significant progress has been made in safeguarding deep reinforcement learning (DRL) policies against input perturbations. Developing robust DRL involves three main stages: algorit…
cs.LG2025
Constrained Black-Box Attacks Against Cooperative Multi-Agent Reinforcement Learning
Amine Andam, Jamal Bentahar, Mustapha Hedabou
Collaborative multi-agent reinforcement learning has rapidly evolved, offering state-of-the-art algorithms for real-world applications, including sensitive domains. However, a key…