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cs.LG2026
RoAd-RL: A Unified Library and Benchmark for Robust Adversarial Reinforcement Learning
Adithya Mohan, Daniel Kriegl, Torsten Schön
Deep Reinforcement Learning (DRL) has achieved significant success in robotics and autonomous systems, yet remains vulnerable to adversarial perturbations that can severely degrade…
cs.LG2025
Advancing Robustness in Deep Reinforcement Learning with an Ensemble Defense Approach
Adithya Mohan, Dominik RöÃle, Daniel Cremers +1
Recent advancements in Deep Reinforcement Learning (DRL) have demonstrated its applicability across various domains, including robotics, healthcare, energy optimization, and autono…