Yuichi Motai

Actively selecting and fine-tuning policies can leverage limited online interactions to achieve superior performance in reinforcement learning, outperforming conventional methods.

Stop committing to a single policy in offline-to-online RL: adaptively select and fine-tune policies based on predicted performance to maximize returns under interaction budgets.