A novel semi-offline Reinforcement Learning (RL) paradigm is proposed to smoothly transition between offline and online settings, optimizing the trade-off between exploration capabilities and training costs.
The research introduces semi-offline RL, a new methodology in reinforcement learning designed to bridge the gap between fully offline and fully online learning settings. Online methods are costly in exploration time, while offline methods sacrifice exploration. This proposed framework effectively balances these considerations, offering a theoretical foundation for comparing different RL settings based on optimization cost, asymptotic error, and overfitting error bounds. Experiments demonstrate that the semi-offline approach is highly efficient and achieves performance comparable to or better than state-of-the-art methods.