دانلود کتاب تقویت یادگیری از ابتدا – درک رویکردهای فعلی – با مثال هایی در جاوا و گرین فوت

Regarding the quotation above, perhaps the content in this book is not quite reduced to a beginner’s level, but it should be very suitable, especially for newcom¬ers to machine learning. It is a book for those with some basic knowledge of pro¬gramming and high school level math. It is useful, for example, for continuing education for teachers and instructors who want to gain insight into programming adaptive agents. The book should also be appropriate for technicians, computer sci¬entists, or programmers who want to better understand RL algorithms by studying and implementing learning these algorithms from scratch themselves (especially if they have been socialized with Java) or for students who want to study machine learning and intelligent agents.
Reinforcement Learning is an area of Machine Learning and belongs to the broad field of Artificial Intelligence. Many believe that reinforcement learning has something to do with artificial neural networks. Although the book also deals with reinforcement learning algorithms that use neural networks, this does not play a central role. Deep reinforcement learning is actually a subset of reinforcement learning, where some functions are implemented with deep neural networks. It may surprise that explanations of the “Asynchronous Advantage Actor-Critic (A3C)” or “Proximal Policy Optimization (PPO)” are possible without the use of neural networks.
We will not dwell on an overly detailed description of mathematical relation¬ships but rather do our own implementations from scratch. Let us use an analogy with a tool such as Google Maps. There are works that are zoomed out very far. They draw an overview that may be completely devoid of mathematics and concrete implementations. This can be useful when you want to give an impression of the landscape. give an impression of the landscape. On the other hand, there are works that get down to the details, some of them want to enable on the use of tools applica¬tion oriented, quasi enable to plan a concrete route through a real city, others focus mainly on a detailed description of the theory.
My goal is for them to get an understanding of the central processes involved in reinforcement learning. If you want to teach how a gasoline engine works, you do not have to analyze a high-performance model. A rough model showing the move¬ment of the pistons, etc., is sufficient. In addition, there are some essential formulas that deepen the insight, but we will mainly do our own constructions. For me, the best teaching model is the one that teaches the ability to model by yourself. To really understand things, it is necessary to do them yourself. It was also an important moti¬vation for me to reproduce the algorithms in my “native” programming language (Java) in order to understand them better.
An added benefit of this exercise for the reader is that he or she can apply the knowledge to a variety of frameworks, with a good understanding of what each parameter means or what the functions do, and perhaps even work out his or her own custom designs. Therefore, the book is particularly useful if you have been socialized with C or Java style languages and, for example, may want or need to use reinforcement learning libraries in another language such as Python. Java has the image of being somewhat complicated, but there are also some very sophisticated tools for playing with and learning the language. An important and widely used tool developed for teaching object-oriented programming with Java is Greenfoot, which we will use here to program adaptive behavior to some Greenfoot-”actors”. We will consider them as interacting agents that are part of (two dimensional) worlds that can created and controlled completely by the learner. I would like to thank Michael Kölling (King’s College London) very much, chief designer of Greenfoot, for his comments on this book. I found them insightful, encouraging and motivating.
I would like to dedicate this book to my children Jonas, Elias, Paul and Julia. I promise that now that this project has come to its conclusion, we will do more together again. I would like to thank my wife Anja, without her support this project would not have been possible. I would also like to thank my sister Ulrike aka “HiroNoUnmei” (https://www.patreon.com/hironounmei; April, 2022) for the funny hamster illustrations at the beginning of each chapter.
The accompanying materials (Java programs, explanatory videos, etc.) are acces¬sible via the product page of the book at SpringerLink. Through the site (https://www.facebook.com/ReinforcementLearningJava; April, 2022), it may also be pos¬sible to build a small community and initiate an exchange about the contents of the book and the topic of reinforcement learning. Feel free to post substantive contribu¬tions on the topic or interesting results. You may also find the opportunity to ask comprehension questions or address points that remain open.