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- Use TensorFlow to write reinforcement learning agents for performing challenging tasks
- Learn how to solve finite Markov decision problems
- Train models to understand popular video games like Breakout

Various intelligent applications such as video games, inventory management software, warehouse robots, and translation tools use reinforcement learning (RL) to make decisions and perform actions that maximize the probability of the desired outcome. This book will help you to get to grips with the techniques and the algorithms for implementing RL in your machine learning models.
Starting with an introduction to RL, youÔÇÖll be guided through different RL environments and frameworks. YouÔÇÖll learn how to implement your own custom environments and use OpenAI baselines to run RL algorithms. Once youÔÇÖve explored classic RL techniques such as Dynamic Programming, Monte Carlo, and TD Learning, youÔÇÖll understand when to apply the different deep learning methods in RL and advance to deep Q-learning. The book will even help you understand the different stages of machine-based problem-solving by using DARQN on a popular video game Breakout. Finally, youÔÇÖll find out when to use a policy-based method to tackle an RL problem.
By the end of The Reinforcement Learning Workshop, youÔÇÖll be equipped with the knowledge and skills needed to solve challenging problems using reinforcement learning.

Use OpenAI Gym as a framework to implement RL environments
Find out how to define and implement reward function
Explore Markov chain, Markov decision process, and the Bellman equation
Distinguish between Dynamic Programming, Monte Carlo, and Temporal Difference Learning
Understand the multi-armed bandit problem and explore various strategies to solve it
Build a deep Q model network for playing the video game Breakout

Download this book in **EPUB** and **PDF** formats

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Publication date :
Aug 18, 2020

Length
822 pages

Edition :
1st Edition

Language :
English

ISBN-13 :
9781800200456

Vendor :

Google

Category :

Languages :

Concepts :

Preface

1. Introduction to Reinforcement Learning

2. Markov Decision Processes and Bellman Equations

3. Deep Learning in Practice with TensorFlow 2

4. Getting Started with OpenAI and TensorFlow for Reinforcement Learning

5. Dynamic Programming

6. Monte Carlo Methods

7. Temporal Difference Learning

8. The Multi-Armed Bandit Problem

9. What Is Deep Q-Learning?

10. Playing an Atari Game with Deep Recurrent Q-Networks

11. Policy-Based Methods for Reinforcement Learning

12. Evolutionary Strategies for RL

Appendix

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