The article titled "Sample Complexity of Distributionally Robust Average-Reward Reinforcement Learning," published on arXiv, investigates the topic of distributionally robust reinforcement learning with a focus on average-reward settings (F1). It highlights the relevance of this approach in critical application fields such as robotics and healthcare, where long-term performance is essential (F2). The authors contribute by introducing two novel algorithms designed to achieve near-optimal sample complexity, addressing a key challenge in reinforcement learning (F3). These algorithms demonstrate promising performance improvements, suggesting enhanced efficiency in learning robust policies under uncertainty (F4). The overarching goal of the research is to improve the reliability and effectiveness of reinforcement learning models in practical, real-world scenarios (F5). The positive stance on the proposed algorithms’ sample complexity underscores their potential impact in advancing distributionally robust reinforcement learning methods (A1). This work aligns with ongoing efforts to develop more resilient AI systems capable of operating effectively in complex environments.