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)
26
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27
Index

Real-life value iteration

The improvements that we got in the FrozenLake environment by switching from the cross-entropy method to the value iteration method are quite encouraging, so it's tempting to apply the value iteration method to more challenging problems. However, let's first look at the assumptions and limitations that our value iteration method has.

We will start with a quick recap of the method. On every step, the value iteration method does a loop on all states, and for every state, it performs an update of its value with a Bellman approximation. The variation of the same method for Q-values (values for actions) is almost the same, but we approximate and store values for every state and action. So, what's wrong with this process?

The first obvious problem is the count of environment states and our ability to iterate over them. In value iteration, we assume that we know all states in our environment in advance, can iterate over them, and can store value...