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Deep Reinforcement Learning Hands-On

Deep Reinforcement Learning Hands-On

By : Maxim Lapan
4.3 (38)
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Deep Reinforcement Learning Hands-On

Deep Reinforcement Learning Hands-On

4.3 (38)
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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26
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27
Index

Values and policy

Before we start talking about policy gradients, let's refresh our minds with the common characteristics of the methods covered in part two of this book. The central topic in value iteration and Q-learning is the value of the state (V) or value of the state and action (Q). Value is defined as the discounted total reward that we can gather from this state or by issuing this particular action from the state. If we know the value, our decision on every step becomes simple and obvious: we just act greedily in terms of value, and that guarantees us a good total reward at the end of the episode. So, the values of states (in the case of the value iteration method) or state + action (in the case of Q-learning) stand between us and the best reward. To obtain these values, we have used the Bellman equation, which expresses the value on the current step via the values on the next step.

In Chapter 1, What Is Reinforcement Learning?, we defined the entity that tells us...

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