系统梳理持续强化学习的关键进展与挑战，助研究者快速入门。
Advancements and Challenges in Continual Reinforcement Learning: A Comprehensive Review
- 梳理持续强化学习的核心概念与方法
- 总结机器人领域最新技术进展
- 适合刚入行的研究者快速了解该领域
强化学习（RL）面临任务多样性和环境动态性，要求智能体具备连续、顺序学习能力，即持续强化学习。本文综述了如何将强化学习智能体转变为动态的持续学习者，使其能无缝获取并保留可复用的知识。文章深入探讨持续强化学习的基本要素，包括关键概念、主要挑战和新方法。特别关注机器人领域近年来的进展，并简要介绍主流研究中使用的评估环境，便于新人进入该领域。最后讨论当前局限与未来潜在方向，为研究人员和实践者提供有益参考。
原文摘要 · Abstract (English)
The diversity of tasks and dynamic nature of reinforcement learning (RL) require RL agents to be able to learn sequentially and continuously, a learning paradigm known as continuous reinforcement learning. This survey reviews how continual learning transforms RL agents into dynamic continual learners. This enables RL agents to acquire and retain useful and reusable knowledge seamlessly. The paper delves into fundamental aspects of continual reinforcement learning, exploring key concepts, significant challenges, and novel methodologies. Special emphasis is placed on recent advancements in continual reinforcement learning within robotics, along with a succinct overview of evaluation environments utilized in prominent research, facilitating accessibility for newcomers to the field. The review concludes with a discussion on limitations and promising future directions, providing valuable insights for researchers and practitioners alike.
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