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Deep Reinforcement Learning Hands on by Lapan Maxim (66 results)
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Paperback. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
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Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more
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Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more
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Add to basketPaperback. Condition: 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.
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Add to basketpaperback. Condition: 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.
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paperback. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
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paperback. Condition: Good. Paperback. Cover and spine in good condition. Spine is tight. Pages are clean, no markings, notes or stains. Book appears to be warped with water damage. Ships from Friends bookstore to benefit Beaverton (Oregon) library.
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Deep Reinforcement Learning Hands-On: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and . web automation, and more, 2nd Edition
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Deep Reinforcement Learning Hands-On: Apply modern RL methods to practical problems of chatbots, robotics, discrete optimization, web automation, and . web automation, and more, 2nd Edition
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paperback. Condition: Good. 2nd ed. Ships in a BOX from Central Missouri! May not include working access code. Will not include dust jacket. Has used sticker(s) and some writing or highlighting. UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes).
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PAP. Condition: New. New Book. Shipped from UK. Established seller since 2000.
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Deep Reinforcement Learning Hands-On
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Deep Reinforcement Learning Hands-On
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Deep Reinforcement Learning Hands-On
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Deep Reinforcement Learning Hands-On (Paperback or Softback)
Published by Packt Publishing 6/20/2018, 2018
ISBN 10: 1788834240 ISBN 13: 9781788834247
Language: English
Paperback or Softback. Condition: New. Deep Reinforcement Learning Hands-On 2.04. Book.
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Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more
Published by Packt Publishing Limited, 2018
ISBN 10: 1788834240 ISBN 13: 9781788834247
Language: English
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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 (Paperback or Softback)
Published by Packt Publishing 11/12/2024, 2024
ISBN 10: 1835882706 ISBN 13: 9781835882702
Language: English
Paperback or Softback. Condition: 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.
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Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more
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Deep Reinforcement Learning Hands-On
Published by Packt Publishing Limited, GB, 2018
ISBN 10: 1788834240 ISBN 13: 9781788834247
Language: English
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
Seller rating 5 out of 5 stars
US$ 78.21
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Add to basketPaperback. Condition: 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.
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US$ 78.78
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