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)
26
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27
Index

The value iteration method

In the simplistic example you just saw, to calculate the values of the states and actions, we exploited the structure of the environment: we had no loops in transitions, so we could start from terminal states, calculate their values, and then proceed to the central state. However, just one loop in the environment builds an obstacle in our approach. Let's consider such an environment with two states:

Figure 5.7: A sample environment with a loop in the transition diagram

We start from state s1, and the only action we can take leads us to state s2. We get the reward r = 1, and the only transition from s2 is an action, which brings us back to s1. So, the life of our agent is an infinite sequence of states . To deal with this infinity loop, we can use a discount factor: . Now, the question is, what are the values for both the states? The answer is not very complicated, in fact. Every transition from s1 to s2 gives us a reward of 1 and every back...