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

Index

Symbols

2×2 cube model 763, 764, 765

3×3 cube model

A

A2C

agent, adding 333, 334, 335

using, on Pong 318, 319, 320, 321, 322, 323, 324

using, on Pong results 324, 325, 326, 327

with data parallelism 334

with gradients parallelism 334

A2C method

about 505

implementation 506, 508, 510

models, used for video recording 512

results 510, 511, 512

A3C, with data parallelism

about 336

implementation 336, 338, 339, 340, 341, 342, 343, 344

result 344

A3C, with with gradients parallelism

about 346, 347

implementation 347, 348, 349, 350, 351, 352

results 352

ACKTR

about 616

implementation 617

results 617, 618

actions 10

action selectors 166, 167

action selectors, cases

argmax 166

policy-based 166

action space 22

actor-critic method 638

about 316, 317

advantage 316

considerations 317, 318

Adam algorithm 322

advantage actor-critic (A2C...