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

Problem statements and key decisions

The finance domain is large and complex, so you can easily spend several years learning something new every day. In our example, we will just scratch the surface a bit with our RL tools, and our problem will be formulated as simply as possible, using price as an observation. We will investigate whether it will be possible for our agent to learn when the best time is to buy one single share and then close the position to maximize the profit. The purpose of this example is to show how flexible the RL model can be and what the first steps are that you usually need to take to apply RL to a real-life use case.

As you already know, to formulate RL problems, three things are needed: observation of the environment, possible actions, and a reward system. In previous chapters, all three were already given to us, and the internal machinery of the environment was hidden. Now we're in a different situation, so we need to decide ourselves what our agent...