Imitation learning heavily relies on the quality of provided demonstrations. In scenarios
where demonstrations are imperfect and rare, a prevalent approach for refining policies is …

In this paper, we study offline-to-online Imitation Learning (IL) that pretrains an imitation
policy from static demonstration data, followed by fast finetuning with minimal environmental …

Offline Reinforcement Learning (RL) endeavors to leverage offline datasets to craft effective
agent policy without online interaction, which imposes proper conservative constraints with …

Combining offline and online reinforcement learning (RL) techniques is indeed crucial for
achieving efficient and safe learning where data acquisition is expensive. Existing methods …

In this paper, we address the challenge of developing advanced motor control systems for
modern washing machines, which are required to operate under various conditions …

Offline-to-online reinforcement learning has recently been shown effective in reducing the
online sample complexity by first training from offline collected data. However, this additional …