Grokking Deep Reinforcement Learning

Grokking Deep Reinforcement Learning

eBook Details:

  • Paperback: 472 pages
  • Publisher: WOW! eBook (October 16, 2020)
  • Language: English
  • ISBN-10: 1617295450
  • ISBN-13: 978-1617295454

eBook Description:

Grokking Deep Reinforcement Learning introduces this powerful machine learning approach, using examples, illustrations, exercises, and crystal-clear teaching. You’ll love the perfectly paced teaching and the clever, engaging writing style as you dig into this awesome exploration of reinforcement learning fundamentals, effective deep learning techniques, and practical applications in this emerging field.

We all learn through trial and error. We avoid the things that cause us to experience pain and failure. We embrace and build on the things that give us reward and success. This common pattern is the foundation of Deep Reinforcement Learning: building machine learning systems that explore and learn based on the responses of the environment.

Deep Reinforcement Learning is a form of machine learning in which AI agents learn optimal behavior on their own from 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. It has been said that Deep Reinforcement Learning, which is the use of deep learning and reinforcement learning techniques to solve problems decision-making problems, is the solution to the full artificial intelligence problem.

Deep Reinforcement Learning famously contributed to the success of AlphaGo and all its successors (AlphaGo, AlphaGo Zero and AlphaZero, etc), which recently beat the world’s best human player in the world’s most difficult board game. But, that is not the only thing you can do with Deep Reinforcement Learning. These are some of the most notable applications:

  • Learn to play ATARI games just by looking at the raw image
  • Learn to trade and manage portfolios effectively
  • Learn low-level control policies for a variety of real-world models
  • Discover tactics and collaborative behavior for improved campaign performance
  • From low-level control, to high-level tactical actions, Deep Reinforcement Learning can solve large, complex decision-making problems

But, Deep Reinforcement Learning is an emerging approach, so the best ideas are still yours to discover. We can’t wait to see how you apply Deep Reinforcement Learning to solve some of the most challenging problems in the world.

What’s inside

  • Foundational reinforcement learning concepts and methods
  • The most popular Deep Reinforcement Learning agents solving high-dimensional environments
  • Cutting-edge agents that emulate human-like behavior and techniques for artificial general intelligence

Grokking Deep Reinforcement Learning is a beautifully balanced approach to teaching, offering numerous large and small examples, annotated diagrams and code, engaging exercises, and skillfully crafted writing. You’ll explore, discover, and learn as you lock in the ins and outs of reinforcement learning, neural networks, and AI agents. You will go from small grid world environments and some of the foundational algorithms to some of the most challenging environments out there today and cutting-edge techniques to solve these environments.

Exciting, fun, and maybe even a little dangerous. Let’s get started!

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6 Responses

  1. October 12, 2020

    […] time to dispel the myth that machine learning is difficult. Grokking Machine Learning teaches you how to apply ML to your projects using only standard Python code and […]

  2. November 15, 2020

    […] Grokking Artificial Intelligence Algorithms uses illustrations, exercises, and jargon-free explanations to teach fundamental AI concepts. All you need is the algebra you remember from high school math class. Explore coding challenges like detect­ing bank fraud, creating artistic masterpieces, and setting a self-driving car in motion. […]

  3. March 23, 2021

    […] policy gradient algorithms, actor-critic methods, model-based methods, and multi-agent advanced deep reinforcement learning. Then, you’ll be introduced to some of the key approaches behind the most successful RL […]

  4. March 23, 2021

    […] policy gradient algorithms, actor-critic methods, model-based methods, and multi-agent advanced deep reinforcement learning. Then, you’ll be introduced to some of the key approaches behind the most successful RL […]

  5. April 2, 2021

    […] Deep Reinforcement Learning in Unity provides a walk-through of the core fundamentals of deep reinforcement learning algorithms, especially variants of the value estimation, advantage, and policy gradient algorithms (including the differences between on and off policy algorithms in reinforcement learning). These core algorithms include actor critic, proximal policy, and deep deterministic policy gradients and its variants. And you will be able to write custom neural networks using the Tensorflow and Keras frameworks. […]

  6. September 13, 2021

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