Easy-RL: A Comprehensive Guide to Reinforcement Learning
Easy-RL, affectionately known as the "Mushroom Book," serves as an essential resource for anyone interested in learning reinforcement learning. It integrates insights from several key contributors and offers a cohesive understanding of reinforcement learning (RL), a type of machine learning in which an agent learns to make decisions by interacting with an environment.
Background and Motivation
The project draws heavily from the teachings of Professor Hung-Yi Lee, whose course on Deep Reinforcement Learning has been pivotal in the field. His engaging teaching style brings the complex theories of RL to life with amusing examples, such as using Atari games to explain RL algorithms. For completeness, Easy-RL also incorporates Zhou Bolei's "Introduction to Reinforcement Learning" and Kejiao Li's "Practice Reinforcement Learning from Scratch" along with other classical resources in the field.
The "Mushroom Book" metaphorically suggests that like the game character Mario who powers up by consuming mushrooms, readers, too, should find their understanding of RL enriched and powerful after engaging with this material.
Usage Guide
Easy-RL is well-structured, offering content through both text and interactive formats:
- Chapters 4 to 11 are based on Professor Lee's "Deep Reinforcement Learning" course.
- Chapters 1 and 2 draw on Zhou Bolei's "Introduction to Reinforcement Learning".
- Chapters 3 and 12 are derived from Kejiao Li's practice-oriented reinforcement learning course.
Formats Available
- Online Reading: Accessible through this link.
- PDF Versions: Available for download here. There are also compressed versions available, ideal for readers with slower internet connections.
Print Edition
Easy-RL is available in print, offering a carefully edited version courtesy of Poste & Telecom Press. The physical book enhances the accessibility of the complex concepts of RL.
Chapter Navigation
The course breaks down into chapters focusing on various aspects of RL, such as:
- Basic RL fundamentals
- Markov Decision Processes
- Advanced strategies like Policy Gradient, PPO (Proximal Policy Optimization), DQN (Deep Q-Network), Actor-Critic Methods, Sparse Reward Challenge, and Imitation Learning.
Each chapter offers exercises and meticulously crafted projects to aid in understanding, enhancing practical learning.
Practical Algorithm Implementation
The project includes a section on algorithmic practice featuring supportive code for learners to delve into real-world applications of the theory they learn.
Additional Resources
Easy-RL also highlights related projects like JoyRL, and provides a platform for engaging with seminal RL papers.
Contributors
Crafted by an exceptional team consisting of Qi Wang, Yiyuan Yang, and John Jim, this guide is supported by their robust academic backgrounds from esteemed institutions like Shanghai Jiao Tong University, Oxford University, and Peking University.
Citations
When referencing Easy-RL in academic work, appropriate citation formats in English and Chinese are provided to ensure the creators receive due credit for their contributions.
Community Engagement
Developed under the banner of Datawhale, Easy-RL aspires to create a dynamic learning community, offering further engagement through a dedicated reader's group.
License
Easy-RL is shared under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License, promoting open learning with appropriate credit to the authors.
By weaving together traditional RL knowledge with modern interpretations and applications, Easy-RL makes learning reinforcement learning an invigorating journey akin to leveling up in one’s academic or professional pursuits in AI.