标题： 非平稳环境下的安全持续强化学习方法。 面向最新技术的综述 显示英文标题

标题： Safe Continual Reinforcement Learning Methods for Nonstationary Environments. Towards a Survey of the State of the Art

摘要： 这项工作提供了持续安全在线强化学习（COSRL）方法的最新综述。 我们讨论了构建持续在线安全强化学习算法中的理论方面、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详细内容。 我们对在线强化学习算法的安全约束公式进行了分类，最后，我们讨论了创建可靠、安全在线学习算法的前景。 关键词：非平稳环境中的安全RL，非平稳下的持续安全强化学习，HM-MDP，NSMDP，POMDP，安全POMDP，持续学习的约束，持续安全强化学习综述，持续安全强化学习调查，持续安全强化学习，分布变化下的安全在线学习，持续在线适应的安全性，安全强化学习，安全探索，安全适应，约束马尔可夫决策过程，安全强化学习，部分可观测马尔可夫决策过程，安全强化学习与隐马尔可夫决策过程，安全在线强化学习，安全在线强化学习，安全在线强化学习，安全元学习，安全元强化学习，基于情境的安全强化学习，持续学习的安全约束制定 显示英文摘要

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