Reinforcement Learning (RL), a subfield of Artificial Intelligence (AI), focuses on training agents to make decisions by interacting with their environment to maximize cumulative rewards. This paper provides an overview of RL, covering its core concepts, methodologies, and resources for further learning. It offers a thorough explanation of fundamental components such as states, actions, policies, and reward signals, ensuring readers develop a solid foundational understanding. Additionally, the paper presents a variety of RL algorithms, categorized based on the key factors such as model-free, model-based, value-based, policy-based, and other key factors. Resources for learning and implementing RL, such as books, courses, and online communities are also provided. By offering a clear, structured introduction, this paper aims to simplify the complexities of RL for beginners, providing a straightforward pathway to understanding and applying real-time techniques.

翻译：强化学习（Reinforcement Learning，RL）作为人工智能（Artificial Intelligence，AI）的一个子领域，专注于通过智能体与环境的交互来训练其做出决策，以最大化累积奖励。本文概述了强化学习的核心概念、方法论及进一步学习资源，详细阐释了状态、动作、策略和奖励信号等基本组成部分，确保读者建立扎实的基础理解。此外，本文介绍了多种强化学习算法，并根据无模型、基于模型、基于价值、基于策略等关键因素进行了分类。同时提供了学习和实践强化学习的资源，包括书籍、课程和在线社区。通过清晰、结构化的介绍，本文旨在为初学者简化强化学习的复杂性，提供一条理解和应用实时技术的直接路径。