系统梳理强化学习算法与实战挑战，助你选对方法解决复杂问题。
A Comprehensive Survey of Reinforcement Learning: From Algorithms to Practical Challenges
- 按可扩展性、样本效率等标准分类评估从基础到深度强化学习方法
- 分析收敛性、稳定性及探索与利用平衡等关键难题的应对策略
- 适合想落地强化学习的研究者与工程师参考
强化学习（RL）已成为人工智能领域的重要范式，使智能体通过与环境交互学习最优行为。基于试错机制，RL让智能体根据奖励或惩罚反馈做出决策。本文全面综述了强化学习，系统分析了从经典表格型方法到先进深度强化学习（DRL）技术的各类算法。我们依据可扩展性、样本效率和适用性等关键指标对算法进行分类与评估，比较其在不同场景下的优劣。同时，针对收敛性、稳定性以及探索-利用权衡等常见挑战，提供实用的算法选择与实现建议。本论文为研究人员与实践者在解决复杂现实问题中充分挖掘强化学习潜力，提供全面参考。
原文摘要 · Abstract (English)
Reinforcement Learning (RL) has emerged as a powerful paradigm in Artificial Intelligence (AI), enabling agents to learn optimal behaviors through interactions with their environments. Drawing from the foundations of trial and error, RL equips agents to make informed decisions through feedback in the form of rewards or penalties. This paper presents a comprehensive survey of RL, meticulously analyzing a wide range of algorithms, from foundational tabular methods to advanced Deep Reinforcement Learning (DRL) techniques. We categorize and evaluate these algorithms based on key criteria such as scalability, sample efficiency, and suitability. We compare the methods in the form of their strengths and weaknesses in diverse settings. Additionally, we offer practical insights into the selection and implementation of RL algorithms, addressing common challenges like convergence, stability, and the exploration-exploitation dilemma. This paper serves as a comprehensive reference for researchers and practitioners aiming to harness the full potential of RL in solving complex, real-world problems.
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