deep reinforcement learning hands on von lapan maxim (62 Ergebnisse)

Zustand: good. Befriedigend/Good: Durchschnittlich erhaltenes Buch bzw. Schutzumschlag mit Gebrauchsspuren, aber vollständigen Seiten. / Describes the average WORN book or dust jacket that has all the pages present.

Paperback. Zustand: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

Zustand: very good. Gut/Very good: Buch bzw. Schutzumschlag mit wenigen Gebrauchsspuren an Einband, Schutzumschlag oder Seiten. / Describes a book or dust jacket that does show some signs of wear on either the binding, dust jacket or pages.

Zustand: Good. Most items will be dispatched the same or the next working day. A copy that has been read but remains in clean condition. All of the pages are intact and the cover is intact and the spine may show signs of wear. The book may have minor markings which are not specifically mentioned. A few small stains.

paperback. Zustand: Used; Very Good. Dispatched, from the UK, within 48 hours of ordering. Though second-hand, the book is still in very good shape. Minimal signs of usage may include very minor creasing on the cover or on the spine.

Zustand: Very Good. Used book that is in excellent condition. May show signs of wear or have minor defects.

Broschiert/Paperback. Zustand: Gut. 523 p. Guter Zustand. Einband mit minimalen Gebrauchsspuren. Seiten sauber. / Good. Cover shows minimal wear. Clean pages. Sprache: Englisch Gewicht in Gramm: 1230.

Kartoniert / Broschiert. Zustand: New. This book is a practical, developer-oriented introduction to deep reinforcement learning (RL). Explore the theoretical concepts of RL, before discovering how deep learning (DL) methods and tools are making it possible to solve more complex and challenging p.

Zustand: Good. Item in good condition. Textbooks may not include supplemental items i.e. CDs, access codes etc.

Paperback. Zustand: Very Good. The book has been read, but is in excellent condition. Pages are intact and not marred by notes or highlighting. The spine remains undamaged.

Zustand: very good. Gut/Very good: Buch bzw. Schutzumschlag mit wenigen Gebrauchsspuren an Einband, Schutzumschlag oder Seiten. / Describes a book or dust jacket that does show some signs of wear on either the binding, dust jacket or pages.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: Good. Item in good condition. Textbooks may not include supplemental items i.e. CDs, access codes etc.

PAP. Zustand: New. New Book. Shipped from UK. Established seller since 2000.

Zustand: New. In English.

Zustand: New.

Paperback or Softback. Zustand: New. Deep Reinforcement Learning Hands-On 2.04. Book.

Zustand: As New. Unread book in perfect condition.

Taschenbuch. Zustand: Neu. Neuware -Publisher's Note: This edition from 2018 is outdated and not compatible with any of the most recent updates to Python libraries. A new third edition, updated for 2020 with six new chapters that include multi-agent methods, discrete optimization, RL in robotics, and advanced exploration techniques is now available.This practical guide will teach you how deep learning (DL) can be used to solve complex real-world problems.Key FeaturesExplore deep reinforcement learning (RL), from the first principles to the latest algorithmsEvaluate high-profile RL methods, including value iteration, deep Q-networks, policy gradients, TRPO, PPO, DDPG, D4PG, evolution strategies and genetic algorithmsKeep up with the very latest industry developments, including AI-driven chatbotsBook DescriptionDeep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross-entropy and policy gradients, before applying them to real-world environments. Take on both the Atari set of virtual games and family favorites such as Connect4.The book provides an introduction to the basics of RL, giving you the know-how to code intelligent learning agents to take on a formidable array of practical tasks. Discover how to implement Q-learning on 'grid world' environments, teach your agent to buy and trade stocks, and find out how natural language models are driving the boom in chatbots.What you will learnUnderstand the DL context of RL and implement complex DL modelsLearn the foundation of RL: Markov decision processesEvaluate RL methods including Cross-entropy, DQN, Actor-Critic, TRPO, PPO, DDPG, D4PG and othersDiscover how to deal with discrete and continuous action spaces in various environmentsDefeat Atari arcade games using the value iteration methodCreate your own OpenAI Gym environment to train a stock trading agentTeach your agent to play Connect4 using AlphaGo ZeroExplore the very latest deep RL research on topics including AI-driven chatbotsWho this book is forSome fluency in Python is assumed. Basic deep learning (DL) approaches should be familiar to readers and some practical experience in DL will be helpful. This book is an introduction to deep reinforcement learning (RL) and requires no background in RL.

Zustand: New.

Zustand: New.

Zustand: New.

Zustand: New. In.

Zustand: As New. Unread book in perfect condition.

Paperback. Zustand: New. This practical guide will teach you how deep learning (DL) can be used to solve complex real-world problems. Key Features Explore deep reinforcement learning (RL), from the first principles to the latest algorithms Evaluate high-profile RL methods, including value iteration, deep Q-networks, policy gradients, TRPO, PPO, DDPG, D4PG, evolution strategies and genetic algorithms Keep up with the very latest industry developments, including AI-driven chatbotsBook DescriptionRecent developments in reinforcement learning (RL), combined with deep learning (DL), have seen unprecedented progress made towards training agents to solve complex problems in a human-like way. Google's use of algorithms to play and defeat the well-known Atari arcade games has propelled the field to prominence, and researchers are generating new ideas at a rapid pace. Deep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross-entropy and policy gradients, before applying them to real-world environments. Take on both the Atari set of virtual games and family favorites such as Connect4. The book provides an introduction to the basics of RL, giving you the know-how to code intelligent learning agents to take on a formidable array of practical tasks. Discover how to implement Q-learning on 'grid world' environments, teach your agent to buy and trade stocks, and find out how natural language models are driving the boom in chatbots.What you will learn Understand the DL context of RL and implement complex DL models Learn the foundation of RL: Markov decision processes Evaluate RL methods including Cross-entropy, DQN, Actor-Critic, TRPO, PPO, DDPG, D4PG and others Discover how to deal with discrete and continuous action spaces in various environments Defeat Atari arcade games using the value iteration method Create your own OpenAI Gym environment to train a stock trading agent Teach your agent to play Connect4 using AlphaGo Zero Explore the very latest deep RL research on topics including AI-driven chatbotsWho this book is forSome fluency in Python is assumed. Basic deep learning (DL) approaches should be familiar to readers and some practical experience in DL will be helpful. This book is an introduction to deep reinforcement learning (RL) and requires no background in RL.

Paperback or Softback. Zustand: 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 2.67. Book.

Paperback. Zustand: New. This practical guide will teach you how deep learning (DL) can be used to solve complex real-world problems. Key Features Explore deep reinforcement learning (RL), from the first principles to the latest algorithms Evaluate high-profile RL methods, including value iteration, deep Q-networks, policy gradients, TRPO, PPO, DDPG, D4PG, evolution strategies and genetic algorithms Keep up with the very latest industry developments, including AI-driven chatbotsBook DescriptionRecent developments in reinforcement learning (RL), combined with deep learning (DL), have seen unprecedented progress made towards training agents to solve complex problems in a human-like way. Google's use of algorithms to play and defeat the well-known Atari arcade games has propelled the field to prominence, and researchers are generating new ideas at a rapid pace. Deep Reinforcement Learning Hands-On is a comprehensive guide to the very latest DL tools and their limitations. You will evaluate methods including Cross-entropy and policy gradients, before applying them to real-world environments. Take on both the Atari set of virtual games and family favorites such as Connect4. The book provides an introduction to the basics of RL, giving you the know-how to code intelligent learning agents to take on a formidable array of practical tasks. Discover how to implement Q-learning on 'grid world' environments, teach your agent to buy and trade stocks, and find out how natural language models are driving the boom in chatbots.What you will learn Understand the DL context of RL and implement complex DL models Learn the foundation of RL: Markov decision processes Evaluate RL methods including Cross-entropy, DQN, Actor-Critic, TRPO, PPO, DDPG, D4PG and others Discover how to deal with discrete and continuous action spaces in various environments Defeat Atari arcade games using the value iteration method Create your own OpenAI Gym environment to train a stock trading agent Teach your agent to play Connect4 using AlphaGo Zero Explore the very latest deep RL research on topics including AI-driven chatbotsWho this book is forSome fluency in Python is assumed. Basic deep learning (DL) approaches should be familiar to readers and some practical experience in DL will be helpful. This book is an introduction to deep reinforcement learning (RL) and requires no background in RL.

Paperback. Zustand: Very Good. Book has light rubbing to covers with faint blemishes to back, light wear to edges, smudging to top textblock. otherwise in great condition, strong binding, bright and unmarked pages; an overall sturdy copy with clean interiors.

paperback. Zustand: Very Good. Open Books is a nonprofit social venture that provides literacy experiences for thousands of readers each year through inspiring programs and creative capitalization of books.

Zustand: Good. Shows minimal wear such as frayed or folded edges, minor rips and tears, and/or slightly worn binding. May have stickers and/or contain inscription on title page. No observed missing pages.