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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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6
Deep Q-Networks
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7
Higher-Level RL Libraries
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15
The TextWorld Environment
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19
Trust Regions – PPO, TRPO, ACKTR, and SAC
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20
Black-Box Optimization in RL
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23
AlphaGo Zero
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26
Other Books You May Enjoy
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27
Index
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Controlling the hardware

In this section, I will describe how we can use the trained model on the real hardware.

MicroPython

For a very long time, the only option in embedded software development was using low-level languages like C or assembly. There are good reasons behind this: limited hardware capabilities, power-efficiency constraints, and the necessity of dealing with real-world events predictably. Using a low-level language, you normally have full control over the program execution and can optimize every tiny detail of your algorithm, which is great.

The downside of this is complexity in the development process, which becomes tricky, error-prone, and lengthy. Even for hobbyist projects that don't have very high efficiency standards, platforms like Arduino offer a quite limited set of languages, which normally includes C and C++.

MicroPython (http://micropython.org) provides an alternative to this low-level development by bringing the Python interpreter to microcontrollers...

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