Q Evaluation

Q-evaluation, a crucial aspect of reinforcement learning, focuses on accurately estimating the performance of a policy using offline data collected under a different policy. Current research emphasizes improving the robustness and efficiency of algorithms like fitted Q-evaluation (FQE), often employing deep neural networks and addressing challenges like unobserved confounders and distribution shifts between the evaluation and behavior policies. These advancements are vital for reliable offline policy evaluation in various applications, from healthcare to robotics, where online experimentation is impractical or impossible. A key trend is the development of new evaluation benchmarks and metrics to rigorously assess the performance of different Q-evaluation methods under diverse conditions.