Adaptive Policy Selection and Fine-Tuning under Interaction Budgets for Offline-to-Online Reinforcement Learning

Offline-to-online reinforcement learning (O2O-RL) involves training policies using pre-collected datasets and then fine-tuning them with limited online interactions. Researchers have developed a method for adaptive policy selection and fine-tuning under interaction budgets for O2O-RL, allowing for more efficient online learning. This approach enables the evaluation of candidate policies via off-policy evaluation (OPE) or online evaluation (OE), selecting the policy with the highest estimated value¹. The method's effectiveness is crucial in scenarios where online interactions are limited, and optimal policy selection is necessary. As state-aligned activity involving reinforcement learning becomes more prevalent, the threat model shifts from criminal to geopolitical, requiring a different approach to security. This development matters to practitioners because it highlights the need for adaptive and efficient reinforcement learning methods that can operate within limited interaction budgets, ultimately impacting the security posture of organizations facing geopolitical threats.