[论文解读] Understanding Reinforcement Learning Algorithms: The Progress from Basic Q-learning to Proximal Policy Optimization

Mohamed-Amine Chadi, Hajar Mousannif|arXiv (Cornell University)|Mar 31, 2023

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一句话总结

一个面向初学者的评估，追踪强化学习概念从Q学习到现代算法如TD3、PPO和离线RL，概述动机、内部机制和局限性。

ABSTRACT

This paper presents a review of the field of reinforcement learning (RL), with a focus on providing a comprehensive overview of the key concepts, techniques, and algorithms for beginners. RL has a unique setting, jargon, and mathematics that can be intimidating for those new to the field or artificial intelligence more broadly. While many papers review RL in the context of specific applications, such as games, healthcare, finance, or robotics, these papers can be difficult for beginners to follow due to the inclusion of non-RL-related work and the use of algorithms customized to those specific applications. To address these challenges, this paper provides a clear and concise overview of the fundamental principles of RL and covers the different types of RL algorithms. For each algorithm/method, we outline the main motivation behind its development, its inner workings, and its limitations. The presentation of the paper is aligned with the historical progress of the field, from the early 1980s Q-learning algorithm to the current state-of-the-art algorithms such as TD3, PPO, and offline RL. Overall, this paper aims to serve as a valuable resource for beginners looking to construct a solid understanding of the fundamentals of RL and be aware of the historical progress of the field. It is intended to be a go-to reference for those interested in learning about RL without being distracted by the details of specific applications.

研究动机与目标

• 为新手提供对基本RL原理的清晰、简明概述。
• 梳理从早期Q-learning到当前最先进方法的RL算法演变。
• 突出每种算法/方法的动机、内部机制及局限性。
• 避免应用相关的干扰，作为坚实的基础参考。

提出的方法

• 呈现从1980年代到现代方法的RL算法历史演进。
• 解释每种算法背后的动机、其核心机制及局限性。
• 将呈现与该领域的历史发展保持一致，而非应用特定细节。

实验结果

研究问题

• RQ1 初学者应理解的强化学习基本原理是什么？
• RQ2 RL算法如何从Q-learning发展到如TD3、PPO和离线RL等现代方法？
• RQ3 关键RL算法的动机、内部机制和局限性是什么？
• RQ4 面向初学者的综述如何帮助读者在不受应用特定干扰的情况下理解RL？

主要发现

• 本文提供了对RL概念与技术的面向初学者的综合性概述。
• 它追踪了从Q-learning到当前最先进的TD3、PPO和离线RL的历史演进。
• 对于每种算法，概述其动机、内部机制和局限性，以支持基础理解。