Easy-RL is a Chinese-language reinforcement learning tutorial that teaches deep reinforcement learning theory and algorithms through interactive content and practical examples.
The project addresses the challenge of learning reinforcement learning concepts in Chinese by synthesizing material from multiple authoritative sources, primarily drawing from a video course known for explaining complex theory through accessible examples like Atari game playing. The tutorial covers foundational concepts including Q-learning and SARSA, progresses through deep reinforcement learning algorithms such as DQN, Double DQN, Dueling DQN, Policy Gradient methods, PPO, A3C, DDPG, and TD3, and includes imitation learning. The material is organized into chapters that combine theoretical explanation with practical implementation guidance.
The project suits learners seeking Chinese-language instruction in reinforcement learning who want to progress from foundational concepts to advanced algorithms. It works well for those who benefit from video lectures combined with written material and code examples. The tutorial is available both as online content that receives continuous updates and as a printed book, giving readers flexibility in how they engage with the material.
The project maintains an errata page for corrections and updates to the tutorial content. The repository contains Jupyter Notebook implementations alongside the educational material, supporting hands-on learning. The work draws from established courses and educational resources rather than introducing novel research, positioning it as a curated educational resource that synthesizes existing knowledge into a cohesive Chinese-language curriculum.
