Actor-Critic Alignment for Offline-to-Online Reinforcement Learning.

摘要

-values for actions outside the offline policy are also tamed. As a result, the online fine-tuning can be simply performed as in the standard actor-critic algorithms. We show empirically that the proposed method improves the performance of the fine-tuned robotic agents on various simulated tasks.