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 value of the action

To make our life slightly easier, we can define different quantities, in addition to the value of the state, V(s), as the value of the action, Q(s, a). Basically, this equals the total reward we can get by executing action a in state s and can be defined via V(s). Being a much less fundamental entity than V(s), this quantity gave a name to the whole family of methods called Q-learning, because it is more convenient.

In these methods, our primary objective is to get values of Q for every pair of state and action.

Q for this state, s, and action, a, equals the expected immediate reward and the discounted long-term reward of the destination state. We also can define V(s) via Q(s, a):

This just means that the value of some state equals to the value of the maximum action we can execute from this state. Finally, we can express Q(s, a) recursively (which will be used in Chapter 6, Deep Q-Networks:

In the preceding formula, the index on the...