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» » » The Reinforcement Learning Workshop: Learn how to apply cutting-edge reinforcement learning algorithms to control problems

The Reinforcement Learning Workshop: Learn how to apply cutting-edge reinforcement learning algorithms to control problems

The Reinforcement Learning Workshop: Learn how to apply cutting-edge reinforcement learning algorithms to control problems

English | 2020 | ISBN-13 : 978-1800200456 | 822 Pages | True PDF | 18.46 MB

Start with the basics of reinforcement learning and explore deep learning concepts such as deep Q-learning, deep recurrent Q-networks, and policy-based methods with this practical guide
Key Features

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

Book Description

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, you'll be guided through different RL environments and frameworks. You'll learn how to implement your own custom environments and use OpenAI baselines to run RL algorithms. Once you've explored classic RL techniques such as Dynamic Programming, Monte Carlo, and TD Learning, you'll 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, you'll find out when to use a policy-based method to tackle an RL problem.

By the end of The Reinforcement Learning Workshop, you'll be equipped with the knowledge and skills needed to solve challenging problems using reinforcement learning.
What you will learn

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

Who this book is for

If you are a data scientist, machine learning enthusiast, or a Python developer who wants to learn basic to advanced deep reinforcement learning algorithms, this workshop is for you. A basic understanding of the Python language is necessary.
Table of Contents

Introduction to Reinforcement Learning
Markov Decision Processes and Bellman Equations
Deep Learning in Practice with TensorFlow 2
Getting Started with OpenAI and TensorFlow for Reinforcement Learning
Dynamic Programming
Monte Carlo Methods
Temporal Difference Learning
The Multi-Armed Bandit Problem
What Is Deep Q Learning?
Playing an Atari Game with Deep Recurrent Q Networks
Policy-Based Methods for Reinforcement Learning
Evolutionary Strategies for RL

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