Author: Zen and the Art of Computer Programming
1 Introduction
In the field of reinforcement learning, there exists a set of interrelated concepts and terminology. In order to allow readers to understand the meaning, connection and relationship behind these concepts and terms, and to be able to get started quickly, master its working mechanism, and how to apply it to actual scenarios, we will show readers how to build A reinforcement learning system in which machine learning, statistics, and programming skills are applied to solve real-world problems. This book includes the following chapters:
- 1. Background introduction (Chapter 1)
- 2. Explanation of Basic Concepts and Terminology (Chapter 2)
- 3. Core algorithm principles, specific operation steps and explanation of mathematical formulas (Chapter 3~4)
- 4. Specific code examples and explanations (Chapter 5)
- 5. Future Development Trends and Challenges (Chapter 6)
- 6. Appendix Frequently Asked Questions and Answers (Appendix A). After reading this book, readers can apply the knowledge they have learned to build their own reinforcement learning system, improve machine learning and programming skills, and better understand and master relevant theories and practices in the field of reinforcement learning. I hope that by reading this book, you can gain new knowledge and gain a lot in the field of reinforcement learning!
2. Explanation of basic concepts and terms
Before we formally introduce how to construct a reinforcement learning system, we first need to understand some basic concepts, terms and nouns related to reinforcement learning. These concepts and terminology will help us clearly understand how reinforcement learning works.（1）Agent
In the reinforcement learning system, the agent completes the decision-making process. Each Agent has an action space and a state space, which are used to describe its possible actions and environment states. The agent's action space can be continuous or discrete. Similarly, the state space is generally continuous or discrete, but sometimes it may be