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Deep Reinforcement Learning with Python

You're reading from Deep Reinforcement Learning with Python Master classic RL, deep RL, distributional RL, inverse RL, and more with OpenAI Gym and TensorFlow

Product type Paperback

Published in Sep 2020

Publisher Packt

ISBN-13 9781839210686

Length 760 pages

Edition 2nd Edition

Languages

Python

Tools

Deep Reinforcement Learning

Concepts

Deep Reinforcement Learning

Author (1):

Sudharsan Ravichandiran

View More author details

Table of Contents (22) Chapters

Preface

1. Fundamentals of Reinforcement Learning

2. A Guide to the Gym Toolkit FREE CHAPTER

3. The Bellman Equation and Dynamic Programming

4. Monte Carlo Methods

5. Understanding Temporal Difference Learning

6. Case Study – The MAB Problem

7. Deep Learning Foundations

8. A Primer on TensorFlow

9. Deep Q Network and Its Variants

10. Policy Gradient Method

11. Actor-Critic Methods – A2C and A3C

12. Learning DDPG, TD3, and SAC

13. TRPO, PPO, and ACKTR Methods

14. Distributional Reinforcement Learning

15. Imitation Learning and Inverse RL

16. Deep Reinforcement Learning with Stable Baselines

17. Reinforcement Learning Frontiers

18. Other Books You May Enjoy

19. Index

Appendix 1 – Reinforcement Learning Algorithms

1. Appendix 2 – Assessments

Index

A

ACKTR, math concepts 540

block diagonal matrix 540, 541

block matrix 540

Kronecker product 542

Kronecker product, properties 543

vec operator 543

action 2, 39

actions 14

action space 18, 40, 73, 74

activation function 265

about 267

exploring 267

Rectified Linear Unit (ReLU) function 269, 270

sigmoid function 268

softmax function 270, 271

tanh function 269

activation map 300

Actor Critic 431

actor critic algorithm 428, 429

actor critic class

action, selecting 441

defining 436

global network, updating 440

init method, defining 436, 437, 439

network, building 440

worker network, updating 441

actor critic method

K-FAC, applying 546, 547, 548

overview 424, 425

working 425, 426, 427

Actor Critic using Kronecker-Factored Trust Region (ACKTR) 538, 539

actor network 598, 599

Advantage 431

Advantage Actor Critic (A2C)

about 429, 430

designing...

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Authors (1)

Sudharsan Ravichandiran

Sudharsan Ravichandiran

Sudharsan Ravichandiran is a data scientist and artificial intelligence enthusiast. He holds a Bachelors in Information Technology from Anna University. His area of research focuses on practical implementations of deep learning and reinforcement learning including natural language processing and computer vision. He is an open-source contributor and loves answering questions on Stack Overflow.

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