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cs.RO2025
Adversarial Fine-tuning in Offline-to-Online Reinforcement Learning for Robust Robot Control
Shingo Ayabe, Hiroshi Kera, Kazuhiko Kawamoto
Offline reinforcement learning enables sample-efficient policy acquisition without risky online interaction, yet policies trained on static datasets remain brittle under action-spa…
cs.RO2024
Robustness Evaluation of Offline Reinforcement Learning for Robot Control Against Action Perturbations
Shingo Ayabe, Takuto Otomo, Hiroshi Kera +1
Offline reinforcement learning, which learns solely from datasets without environmental interaction, has gained attention. This approach, similar to traditional online deep reinfor…