Book Image

Deep Reinforcement Learning Hands-On - Second Edition

By : Maxim Lapan
Book Image

Deep Reinforcement Learning Hands-On - Second Edition

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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Index

Categorical DQN

The last, and the most complicated, method in our DQN improvements toolbox is from a very recent paper, published by DeepMind in June 2017, called A Distributional Perspective on Reinforcement Learning ([9] Bellemare, Dabney, and Munos, 2017).

In the paper, the authors questioned the fundamental piece of Q-learning—Q-values—and tried to replace them with a more generic Q-value probability distribution. Let's try to understand the idea. Both the Q-learning and value iteration methods work with the values of the actions or states represented as simple numbers and showing how much total reward we can achieve from a state, or an action and a state. However, is it practical to squeeze all future possible rewards into one number? In complicated environments, the future could be stochastic, giving us different values with different probabilities.

For example, imagine the commuter scenario when you regularly drive from home to work. Most of the time...