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 training process

Now that you know how the state of the cube is encoded in a 20 × 24 tensor, let's talk about the NN architecture and how it is trained.

The NN architecture

On the figure that follows (taken from the paper), the network architecture is shown.

Figure 24.2: The NN architecture transforming the observation (top) to the action and value (bottom)

As the input, it accepts the already familiar cube state representation as a 20 × 24 tensor and produces two outputs:

  • The policy, which is a vector of 12 numbers, representing the probability distribution over our actions.
  • The value, a single scalar estimating the "goodness" of the state passed. The concrete meaning of a value will be discussed later.

Between the input and output, the network has several fully connected layers with exponential linear unit (ELU) activations. In my implementation, the architecture is exactly the same as in the paper, and the model is...