Reinforcement Learning (RL) has shown remarkable success in solving relatively complex tasks, yet the deployment of RL systems in real-world scenarios poses significant challenges related to safety and robustness. This paper aims to identify and further understand those challenges thorough the exploration of the main dimensions of the safe and robust RL landscape, encompassing algorithmic, ethical, and practical considerations. We conduct a comprehensive review of methodologies and open problems that summarizes the efforts in recent years to address the inherent risks associated with RL applications. After discussing and proposing definitions for both safe and robust RL, the paper categorizes existing research works into different algorithmic approaches that enhance the safety and robustness of RL agents. We examine techniques such as uncertainty estimation, optimisation methodologies, exploration-exploitation trade-offs, and adversarial training. Environmental factors, including sim-to-real transfer and domain adaptation, are also scrutinized to understand how RL systems can adapt to diverse and dynamic surroundings. Moreover, human involvement is an integral ingredient of the analysis, acknowledging the broad set of roles that humans can take in this context. Importantly, to aid practitioners in navigating the complexities of safe and robust RL implementation, this paper introduces a practical checklist derived from the synthesized literature. The checklist encompasses critical aspects of algorithm design, training environment considerations, and ethical guidelines. It will serve as a resource for developers and policymakers alike to ensure the responsible deployment of RL systems in many application domains.

翻译：强化学习（RL）在解决相对复杂任务方面已展现出显著成效，但在现实场景中部署RL系统却面临安全性与鲁棒性相关的重大挑战。本文旨在通过探索安全与鲁棒RL领域的主要维度（涵盖算法、伦理及实践考量），识别并进一步理解这些挑战。我们系统梳理了近些年来应对RL应用固有风险的方法与开放性问题，归纳了相关研究进展。在讨论并提出安全RL与鲁棒RL的定义后，本文将现有研究工作分类为增强RL代理安全性与鲁棒性的不同算法路径。我们考察了不确定性估计、优化方法、探索-利用权衡及对抗训练等技术。同时，对包括模拟到现实迁移和领域自适应在内的环境因素进行深入分析，以理解RL系统如何适应多样化动态环境。此外，人类参与作为分析的核心组成部分，我们承认在此背景下人类可能扮演的广泛角色。重要的是，为帮助从业者应对安全与鲁棒RL实施的复杂性，本文基于综合文献提出一套实用检查清单。该清单涵盖算法设计的关键方面、训练环境考量及伦理准则，可为开发者和政策制定者提供资源，确保RL系统在众多应用领域的负责任部署。