arxiv
PublishedMay 13, 2026 at 4:00 AM
Sparse Offline Reinforcement Learning with Corruption Robustness
Publisher summary· verbatim
arXiv:2512.24768v3 Announce Type: replace-cross Abstract: We investigate robustness to strong data corruption in offline sparse reinforcement learning (RL). In our setting, an adversary may arbitrarily perturb a fraction of the collected trajectories from a high-dimensional but sparse Markov decisio
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Originally published on arxiv ↗