The field of reinforcement learning is moving towards developing more robust and fair methods for real-world applications. Recent research has focused on improving the safety and equity of decision support systems, particularly in population health management. Offline reinforcement learning has emerged as a promising approach, allowing for the development of effective policies from historical data without the need for costly online interactions. Additionally, there is a growing interest in hybrid methods that combine offline and online learning to leverage the strengths of both approaches. Notably, some papers have made significant contributions to the field, including the development of innovative algorithms such as Feasibility-Guided Fair Adaptive Reinforcement Learning and Robust Sparse Sampling, which have shown impressive results in improving fairness and robustness in reinforcement learning. Others, like Conservative Discrete Quantile Actor-Critic, have demonstrated the ability to learn effective scheduling policies from random data, highlighting the potential for reinforcement learning to generalize beyond suboptimality.