Tag: Reinforcement

September 27, 2024

Reinforcement-Based Treatment for Substance Use Disorders A Comprehensive Behavioral Approach

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Free Download L. Michelle Tuten, "Reinforcement-Based Treatment for Substance Use Disorders: A Comprehensive Behavioral Approach"
English | ISBN: 1433810247 | 2011 | 293 pages | PDF | 4 MB
This book is a clinician-friendly manual for implementing Reinforcement-Based Treatment (RBT), an intensive, evidence-based model for treating substance use disorders in community settings.

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September 15, 2024

Algorithms for Reinforcement Learning

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Free Download Csaba Szepesvári, "Algorithms for Reinforcement Learning "
English | ISBN: 303100423X | 2010 | 104 pages | PDF | 3 MB
Reinforcement learning is a learning paradigm concerned with learning to control a system so as to maximize a numerical performance measure that expresses a long-term objective. What distinguishes reinforcement learning from supervised learning is that only partial feedback is given to the learner about the learner’s predictions. Further, the predictions may have long term effects through influencing the future state of the controlled system. Thus, time plays a special role. The goal in reinforcement learning is to develop efficient learning algorithms, as well as to understand the algorithms’ merits and limitations. Reinforcement learning is of great interest because of the large number of practical applications that it can be used to address, ranging from problems in artificial intelligence to operations research or control engineering. In this book, we focus on those algorithms of reinforcement learning that build on the powerful theory of dynamic programming. We give a fairly comprehensive catalog of learning problems, describe the core ideas, note a large number of state of the art algorithms, followed by the discussion of their theoretical properties and limitations. Table of Contents: Markov Decision Processes / Value Prediction Problems / Control / For Further Exploration

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April 26, 2024

The Art of Reinforcement Learning Fundamentals, Mathematics, and Implementations with Python

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Free Download The Art of Reinforcement Learning: Fundamentals, Mathematics, and Implementations with Python by Michael Hu
English | December 9, 2023 | ISBN: 1484296052 | 308 pages | MOBI | 25 Mb
Unlock the full potential of reinforcement learning (RL), a crucial subfield of Artificial Intelligence, with this comprehensive guide. This book provides a deep dive into RL’s core concepts, mathematics, and practical algorithms, helping you to develop a thorough understanding of this cutting-edge technology. Beginning with an overview of fundamental concepts such as Markov decision processes, dynamic programming, Monte Carlo methods, and temporal difference learning, this book uses clear and concise examples to explain the basics of RL theory. The following section covers value function approximation, a critical technique in RL, and explores various policy approximations such as policy gradient methods and advanced algorithms like Proximal Policy Optimization (PPO).

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April 26, 2024

Reinforcement Learning, 2.Auflage

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Free Download Reinforcement Learning: Aktuelle Ansätze verstehen – mit Beispielen in Java und Greenfoot
Deutsch | 2024 | ISBN: 3662683105 | 217 Seiten | PDF (True) | 9 MB
In uralten Spielen wie Schach oder Go können sich die brillantesten Spieler verbessern, indem sie die von einer Maschine produzierten Strategien studieren. Robotische Systeme üben ihre Bewegungen selbst. In Arcade Games erreichen lernfähige Agenten innerhalb weniger Stunden übermenschliches Niveau. Wie funktionieren diese spektakulären Algorithmen des bestärkenden Lernens? Mit gut verständlichen Erklärungen und übersichtlichen Beispielen in Java und Greenfoot können Sie sich die Prinzipien des bestärkenden Lernens aneignen und in eigenen intelligenten Agenten anwenden. Greenfoot (M.Kölling, King’s College London) und das Hamster-Modell (D.Bohles, Universität Oldenburg) sind einfache, aber auch mächtige didaktische Werkzeuge, die entwickelt wurden, um Grundkonzepte der Programmierung zu vermitteln. Wir werden Figuren wie den Java-Hamster zu lernfähigen Agenten machen, die eigenständig ihre Umgebung erkunden. Die zweite Auflage enthält neue Themen wie "Genetische Algorithmen" und "Künstliche Neugier" sowie Korrekturen und Überarbeitungen.

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