ISBN: 9781835882702 - deep reinforcement learning hands-on: a practical and easy-to-follow guide to rl from q-learning and dqns to ppo and rlhf (21 resultados)

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Paperback or Softback. Condición: New. Deep Reinforcement Learning Hands-On - Third Edition: A practical and easy-to-follow guide to RL from Q-learning and DQNs to PPO and RLHF. Book.

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Paperback. Condición: New. This expanded third edition of the popular Deep Reinforcement Learning Hands-On teaches cutting-edge techniques through new projects and over 20 practical chapters, fully updated for PyTorch 2.3, Gymnasium, stable-baselines3, and others.

Paperback. Condición: New. This expanded third edition of the popular Deep Reinforcement Learning Hands-On teaches cutting-edge techniques through new projects and over 20 practical chapters, fully updated for PyTorch 2.3, Gymnasium, stable-baselines3, and others.

Paperback. Condición: New. This expanded third edition of the popular Deep Reinforcement Learning Hands-On teaches cutting-edge techniques through new projects and over 20 practical chapters, fully updated for PyTorch 2.3, Gymnasium, stable-baselines3, and others.

Paperback. Condición: New. This expanded third edition of the popular Deep Reinforcement Learning Hands-On teaches cutting-edge techniques through new projects and over 20 practical chapters, fully updated for PyTorch 2.3, Gymnasium, stable-baselines3, and others.

Condición: New.

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Condición: New.

Condición: As New. Unread book in perfect condition.

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PAP. Condición: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

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Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Maxim Lapan delivers intuitive explanations and insights into complex reinforcement learning (RL) concepts, starting from the basics of RL on simple environments and tasks to modern, state-of-the-art methodsPurchase of the print or Kindle book includes a free PDF Elektronisches BuchFree with your book: DRM-free PDF version + access to Packt's next-gen Reader\*Key Features: Learn with concise explanations, modern libraries, and diverse applications from games to stock trading and web navigation Develop deep RL models, improve their stability, and efficiently solve complex environments New content on RL from human feedback (RLHF), MuZero, and transformersBook Description:Start your journey into reinforcement learning (RL) and reward yourself with the third edition of Deep Reinforcement Learning Hands-On. This book takes you through the basics of RL to more advanced concepts with the help of various applications, including game playing, discrete optimization, stock trading, and web browser navigation. By walking you through landmark research papers in the fi eld, this deep RL book will equip you with practical knowledge of RL and the theoretical foundation to understand and implement most modern RL papers.The book retains its approach of providing concise and easy-to-follow explanations from the previous editions. You'll work through practical and diverse examples, from grid environments and games to stock trading and RL agents in web environments, to give you a well-rounded understanding of RL, its capabilities, and its use cases. You'll learn about key topics, such as deep Q-networks (DQNs), policy gradient methods, continuous control problems, and highly scalable, non-gradient methods.If you want to learn about RL through a practical approach using OpenAI Gym and PyTorch, concise explanations, and the incremental development of topics, then Deep Reinforcement Learning Hands-On, Third Edition, is your ideal companion\*Email sign-up and proof of purchase requiredWhat You Will Learn: Stay on the cutting edge with new content on MuZero, RL with human feedback, and LLMs Evaluate RL methods, including cross-entropy, DQN, actor-critic, TRPO, PPO, DDPG, and D4PG Implement RL algorithms using PyTorch and modern RL libraries Build and train deep Q-networks to solve complex tasks in Atari environments Speed up RL models using algorithmic and engineering approaches Leverage advanced techniques like proximal policy optimization (PPO) for more stable trainingWho this book is for:This book is ideal for machine learning engineers, software engineers, and data scientists looking to learn and apply deep reinforcement learning in practice. It assumes familiarity with Python, calculus, and machine learning concepts. With practical examples and high-level overviews, it's also suitable for experienced professionals looking to deepen their understanding of advanced deep RL methods and apply them across industries, such as gaming and financeTable of Contents What Is Reinforcement Learning OpenAI Gym Deep Learning with PyTorch The Cross-Entropy Method Tabular Learning and the Bellman Equation Deep Q-Networks Higher-Level RL Libraries DQN Extensions Ways to Speed up RL Stocks Trading Using RL Policy Gradients - an Alternative Actor-Critic Methods - A2C and A3C The TextWorld Environment Web Navigation Continuous Action Space Trust Regions - PPO, TRPO, ACKTR, and SAC Black-Box Optimization in RL Advanced Exploration RL with Human Feedback(N.B. Please use the Read Sample option to see further chapters).…

Taschenbuch. Condición: Neu. Deep Reinforcement Learning Hands-On - Third Edition | A practical and easy-to-follow guide to RL from Q-learning and DQNs to PPO and RLHF | Maxim Lapan | Taschenbuch | Englisch | 2024 | Packt Publishing | EAN 9781835882702 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.…