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TensorFlow 2 Reinforcement Learning Cookbook

You're reading from TensorFlow 2 Reinforcement Learning Cookbook

Product type Book

Published in Jan 2021

Publisher Packt

ISBN-13 9781838982546

Pages 472 pages

Edition 1st Edition

Languages

Concepts

Reinforcement Learning

Author (1):

Palanisamy P

Table of Contents (11) Chapters

Preface

1. Chapter 1: Developing Building Blocks for Deep Reinforcement Learning Using Tensorflow 2.x

2. Chapter 2: Implementing Value-Based, Policy-Based, and Actor-Critic Deep RL Algorithms

3. Chapter 3: Implementing Advanced RL Algorithms

4. Chapter 4: Reinforcement Learning in the Real World – Building Cryptocurrency Trading Agents

5. Chapter 5: Reinforcement Learning in the Real World – Building Stock/Share Trading Agents

6. Chapter 6: Reinforcement Learning in the Real World – Building Intelligent Agents to Complete Your To-Dos

7. Chapter 7: Deploying Deep RL Agents to the Cloud

8. Chapter 8: Distributed Training for Accelerated Development of Deep RL Agents

9. Chapter 9: Deploying Deep RL Agents on Multiple Platforms

10. Other Books You May Enjoy

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Mastering Reinforcement Learning with Python

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ISBN: 978-1-83864-414-7

Model and solve complex sequential decision-making problems using RL
Develop a solid understanding of how state-of-the-art RL methods work
Use Python and TensorFlow to code RL algorithms from scratch
Parallelize and scale up your RL implementations using Ray's RLlib package
Get in-depth knowledge of a wide variety of RL topics
Understand the trade-offs between different RL approaches
Discover and address the challenges of implementing RL in the real world

Deep Reinforcement Learning with Python - Second Edition

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ISBN: 978-1-83921-068-6

Understand core RL concepts including the methodologies, math, and code
Train an agent to solve Blackjack, FrozenLake, and many other problems using OpenAI Gym

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Authors (1)

Palanisamy P

Praveen Palanisamy works on developing autonomous intelligent systems. He is currently an AI researcher at General Motors R&D. He develops planning and decision-making algorithms and systems that use deep reinforcement learning for autonomous driving. Previously, he was at the Robotics Institute, Carnegie Mellon University, where he worked on autonomous navigation, including perception and AI for mobile robots. He has experience developing complete, autonomous, robotic systems from scratch.

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