Learning reinforcement theory (98 Ergebnisse)

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Paperback or Softback. Zustand: New. Foundations of Deep Reinforcement Learning: Theory and Practice in Python. Book.

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Taschenbuch. Zustand: Neu. Neuware - The Contemporary Introduction to Deep Reinforcement Learning that Combines Theory and PracticeDeep reinforcement learning (deep RL) combines deep learning and reinforcement learning, in which artificial agents learn to solve sequential decision-making problems. In the past decade deep RL has achieved remarkable results on a range of problems, from single and multiplayer gamessuch as Go, Atari games, and DotA 2to robotics.Foundations of Deep Reinforcement Learning is an introduction to deep RL that uniquely combines both theory and implementation. It starts with intuition, then carefully explains the theory of deep RL algorithms, discusses implementations in its companion software library SLM Lab, and finishes with the practical details of getting deep RL to work.This guide is ideal for both computer science students and software engineers who are familiar with basic machine learning concepts and have a working understanding of Python.- Understand each key aspect of a deep RL problem- Explore policy- and value-based algorithms, including REINFORCE, SARSA, DQN, Double DQN, and Prioritized Experience Replay (PER)- Delve into combined algorithms, including Actor-Critic and Proximal Policy Optimization (PPO)- Understand how algorithms can be parallelized synchronously and asynchronously- Run algorithms in SLM Lab and learn the practical implementation details for getting deep RL to work- Explore algorithm benchmark results with tuned hyperparameters- Understand how deep RL environments are designedRegister your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details. …

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Taschenbuch. Zustand: Neu. Fuzzy Reinforcement Learning Based Controller Design | Lyapunov Theory based Reinforcement Learning Controller for Non Linear Systems | Abhishek Kumar | Taschenbuch | Englisch | 2021 | LAP LAMBERT Academic Publishing | EAN 9786204732015 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu. …

Taschenbuch. Zustand: Neu. Druck auf Anfrage Neuware - Printed after ordering - Reinforcement Learning: Theory and Python Implementation is a tutorial book on reinforcement learning, with explanations of both theory and applications. Starting from a uniform mathematical framework, this book derives the theory of modern reinforcement learning systematically and introduces all mainstream reinforcement learning algorithms such as PPO, SAC, and MuZero. It also covers key technologies of GPT training such as RLHF, IRL, and PbRL. Every chapter is accompanied by high-quality implementations, and all implementations of deep reinforcement learning algorithms are with both TensorFlow and PyTorch. Codes can be found on GitHub along with their results and are runnable on a conventional laptop with either Windows, macOS, or Linux.This book is intended for readers who want to learn reinforcement learning systematically and apply reinforcement learning to practical applications. It is also ideal to academical researchers who seek theoretical foundation or algorithm enhancement in their cutting-edge AI research.…

Taschenbuch. Zustand: Neu. Neuware - This book is a comprehensive guide to one of the most exciting fields in artificial intelligence. This book blends foundational theory with cutting-edge algorithms and real-world applications, offering readers a practical and intuitive understanding of how intelligent agents learn through interaction.Designed for students, researchers, and professionals, the book covers key concepts such as Markov decision processes, dynamic programming, Monte Carlo methods, temporal difference learning, deep reinforcement learning, and multi-agent systems. With a balance of mathematical rigor and accessible explanations, it serves as both a learning resource and a reference guide for applying RL to solve complex problem across domains.…

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Taschenbuch. Zustand: Neu. Towards Fuzzy Learning Classifier Systems | Theory and Application of the Reinforcement Learning, Fuzzy Logic and Learning Classifier Systems | Daniel Hládek (u. a.) | Taschenbuch | 124 S. | Englisch | 2012 | LAP LAMBERT Academic Publishing | EAN 9783847311355 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.…

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