HandsOn Reinforcement Learning with R
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Section 1  Getting Started with Reinforcement Learning with R

Overview of Reinforcement Learning with R

Building Blocks of Reinforcement Learning

Section 2  Reinforcement Learning Algorithms and Techniques

Markov Decision Processes in Action

MultiArmed Bandit Models

Dynamic Programming for Optimal Policies
 Dynamic Programming for Optimal Policies
 Technical requirements
 Understanding DP
 Learning the topdown DP approach
 Analyzing the difference between recursion and memoization
 Learning the optimization techniques
 Implementing DP in reinforcement learning applications
 Solving the knapsack problem
 Optimization of a robot navigation system
 Summary

Monte Carlo Methods for Predictions

Temporal Difference Learning

Section 3  RealWorld Applications

Reinforcement Learning in Game Applications

MAB for Financial Engineering

TD Learning in Healthcare

Section 4  Deep Reinforcement Learning

Exploring Deep Reinforcement Learning Methods

Deep QLearning Using Keras

Whats Next?

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About this book
Reinforcement learning (RL) is an integral part of machine learning (ML), and is used to train algorithms. With this book, you'll learn how to implement reinforcement learning with R, exploring practical examples such as using tabular Qlearning to control robots.
You'll begin by learning the basic RL concepts, covering the agentenvironment interface, Markov Decision Processes (MDPs), and policy gradient methods. You'll then use R's libraries to develop a model based on Markov chains. You will also learn how to solve a multiarmed bandit problem using various R packages. By applying dynamic programming and Monte Carlo methods, you will also find the best policy to make predictions. As you progress, you'll use Temporal Difference (TD) learning for vehicle routing problem applications. Gradually, you'll apply the concepts you've learned to realworld problems, including fraud detection in finance, and TD learning for planning activities in the healthcare sector. You'll explore deep reinforcement learning using Keras, which uses the power of neural networks to increase RL's potential. Finally, you'll discover the scope of RL and explore the challenges in building and deploying machine learning models.
By the end of this book, you'll be wellversed with RL and have the skills you need to efficiently implement it with R.
 Publication date:
 December 2019
 Publisher
 Packt
 Pages
 362
 ISBN
 9781789616712