非平稳环境下的安全持续强化学习方法：对当前最先进方法的综述
Safe Continual Reinforcement Learning Methods for Nonstationary Environments. Towards a Survey of the State of the Art
AI总结：
本文综述了非平稳环境下安全持续强化学习的最新方法，探讨了理论挑战和未来发展方向。
摘要：
本文提供了一项关于持续安全在线强化学习（COSRL）方法的最先进综述。我们讨论了构建持续在线安全强化学习算法的理论方面、挑战和开放问题。我们根据考虑适应非平稳性的安全学习机制类型，提供了持续在线安全强化学习方法的分类和细节。我们对在线强化学习算法的安全约束公式化进行了分类，最后讨论了创建可靠、安全在线学习算法的前景。
英文摘要：
This work provides a state-of-the-art survey of continual safe online reinforcement learning (COSRL) methods. We discuss theoretical aspects, challenges, and open questions in building continual online safe reinforcement learning algorithms. We provide the taxonomy and the details of continual online safe reinforcement learning methods based on the type of safe learning mechanism that takes adaptation to nonstationarity into account. We categorize safety constraints formulation for online reinforcement learning algorithms, and finally, we discuss prospects for creating reliable, safe online learning algorithms. Keywords: safe RL in nonstationary environments, safe continual reinforcement learning under nonstationarity, HM-MDP, NSMDP, POMDP, safe POMDP, constraints for continual learning, safe continual reinforcement learning review, safe continual reinforcement learning survey, safe continual reinforcement learning, safe online learning under distribution shift, safe continual online adaptation, safe reinforcement learning, safe exploration, safe adaptation, constrained Markov decision processes, safe reinforcement learning, partially observable Markov decision process, safe reinforcement learning and hidden Markov decision processes, Safe Online Reinforcement Learning, safe online reinforcement learning, safe online reinforcement learning, safe meta-learning, safe meta-reinforcement learning, safe context-based reinforcement learning, formulating safety constraints for continual learning
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
