Deep Reinforcement Learning Hands-On – Second Edition

Deep Reinforcement Learning Hands-On, 2nd Edition

eBook Details:

  • Paperback: 826 pages
  • Publisher: WOW! eBook (January 31, 2020)
  • Language: English
  • ISBN-10: 1838826998
  • ISBN-13: 978-1838826994

eBook Description:

Deep Reinforcement Learning Hands-On, 2nd Edition: New edition of the bestselling guide to deep reinforcement learning and how it’s used to solve complex real-world problems. Revised and expanded to include multi-agent methods, discrete optimization, RL in robotics, advanced exploration techniques, and more

Deep Reinforcement Learning Hands-On, Second Edition is an updated and expanded version of the bestselling guide to the very latest reinforcement learning (RL) tools and techniques. It provides you with an introduction to the fundamentals of RL, along with the hands-on ability to code intelligent learning agents to perform a range of practical tasks.

With six new chapters devoted to a variety of up-to-the-minute developments in RL, including discrete optimization (solving the Rubik’s Cube), multi-agent methods, Microsoft’s TextWorld environment, advanced exploration techniques, and more, you will come away from this book with a deep understanding of the latest innovations in this emerging field.

In addition, you will gain actionable insights into such topic areas as deep Q-networks, policy gradient methods, continuous control problems, and highly scalable, non-gradient methods. You will also discover how to build a real hardware robot trained with RL for less than $100 and solve the Pong environment in just 30 minutes of training using step-by-step code optimization.

  • Understand the deep learning context of RL and implement complex deep learning models
  • Evaluate RL methods including cross-entropy, DQN, actor-critic, TRPO, PPO, DDPG, D4PG, and others
  • Build a practical hardware robot trained with RL methods for less than $100
  • Discover Microsoft’s TextWorld environment, which is an interactive fiction games platform
  • Use discrete optimization in RL to solve a Rubik’s Cube
  • Teach your agent to play Connect 4 using AlphaGo Zero
  • Explore the very latest deep RL research on topics including AI chatbots
  • Discover advanced exploration techniques, including noisy networks and network distillation techniques

In short, Deep Reinforcement Learning Hands-On, Second Edition, is your companion to navigating the exciting complexities of RL as it helps you attain experience and knowledge through real-world examples.


5 Responses

  1. March 5, 2020

    […] Deep Reinforcement Learning is a form of machine learning in which AI agents learn optimal behavior from their own raw sensory input. The system perceives the environment, interprets the results of its past decisions, and uses this information to optimize its behavior for maximum long-term return. Deep Reinforcement Learning famously contributed to the success of AlphaGo but that’s not all it can do! More exciting applications wait to be discovered. Let’s get started. […]

  2. March 11, 2020

    […] Reinforcement Learning Quick Start Guide: Leverage the power of Tensorflow to Create powerful software agents that can […]

  3. June 22, 2020

    […] Reinforcement Learning – develop systems that can solve complex problems such as driving or game playing […]

  4. June 22, 2020

    […] Reinforcement Learning – develop systems that can solve complex problems such as driving or game playing […]

  5. October 4, 2020

    […] of OpenAI’s baseline library, to effortlessly implement popular RL algorithms. The Hands-On Reinforcement Learning with Python, 2nd Edition book concludes with an overview of promising approaches such as […]

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