A recent study introduces an adaptive neighborhood-constrained Q learning method to improve offline reinforcement learning by tackling the issue of extrapolation errors caused by out-of-distribution actions. The research categorizes existing constraints into three types: density, support, and sample constraints, and critically examines their limitations in effectively guiding action selection. These limitations highlight challenges in current approaches, which the study observes as insufficient for robust policy learning. To address these shortcomings, the authors propose enhancements aimed at more effective constraint design, thereby improving the reliability of offline reinforcement learning algorithms. This approach represents a positive advancement in mitigating errors associated with offline policy evaluation and optimization. The study’s insights contribute to ongoing efforts in the AI community to refine reinforcement learning techniques for better performance in offline settings.