Book Image

Deep Reinforcement Learning Hands-On - Second Edition

By : Maxim Lapan
5 (2)
Book Image

Deep Reinforcement Learning Hands-On - Second Edition

5 (2)
By: Maxim Lapan

Overview of this book

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. 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.
Table of Contents (28 chapters)
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The model application

Okay, imagine that we have trained the model using the process just described. How should we use it to solve the scrambled cube? From the network's structure, you might imagine the obvious, but not very successful, way:

  1. Feed the model the current state of the cube that we want to solve
  2. From the policy head, get the largest action to perform (or sample it from the resulting distribution)
  3. Apply the action to the cube
  4. Repeat the process until the solved state has been reached

On paper, this method should work, but in practice, it has one serious issue: it doesn't! The main reason for that is our model's quality. Due to the size of the state space and the nature of the NNs, it just isn't possible to train an NN to return the exact optimal action for any input state all of the time. Rather than telling us what to do to get the solved state, our model shows us promising directions to explore. Those directions could bring...