Policy Identification

Policy identification in reinforcement learning focuses on efficiently finding near-optimal policies within various Markov Decision Process (MDP) frameworks, aiming to minimize sample complexity and computational cost. Current research emphasizes developing model-free algorithms, such as those employing policy gradient methods or best-arm identification techniques, and analyzing instance-dependent sample complexity to better understand the inherent difficulty of specific problems. These advancements are crucial for deploying reinforcement learning in real-world applications where data acquisition is expensive and robustness to uncertainty is paramount, impacting fields like robotics and personalized medicine.