Policy Evaluation

Policy evaluation assesses the performance of a decision-making policy, typically using data collected from prior interactions, a crucial step in reinforcement learning and related fields. Current research emphasizes improving the accuracy and efficiency of policy evaluation, particularly addressing challenges like high variance in off-policy estimators (using data from a different policy than the one being evaluated), bias from interference between agents, and the need for interpretable results. These advancements are vital for safely deploying learned policies in real-world applications such as healthcare, robotics, and recommender systems, where direct online evaluation may be impractical or risky.