A Beginner's Guide to Reinforcement Learning: From Agents to Markov Property and Gym Environments
English summary
This tutorial provides a beginner-friendly introduction to reinforcement learning, covering core concepts from agents and rewards to the Markov property. It then walks through setting up a first Gym environment, designed as a set of concise, note-style explanations for newcomers.
Chinese summary
本教程以初学者友好的方式介绍强化学习，涵盖智能体、奖励、马尔可夫性质等核心概念，并引导设置第一个Gym环境，以笔记式简练讲解帮助入门者快速上手。
Key points
Covers fundamental RL concepts: agents, rewards, and the Markov property.
涵盖强化学习基础概念：智能体、奖励以及马尔可夫性质。
Includes a hands-on introduction to setting up a Gym environment for RL experiments.
包含上手搭建Gym环境的实践引导，用于强化学习实验。
Written in an informal, note-like style to make RL accessible to absolute beginners.
采用非正式的笔记式风格，让纯初学者也能轻松理解强化学习。