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1.Overview of Reinforcement Learning with R
2. Building Blocks of Reinforcement Learning
3. Markov Decision Processes in Action
4. Multi-Armed Bandit Models
5. Dynamic programming for Optimal Policies
6. Monte-Carlo Methods for Prediction
7. Temporal Difference Learning
8. Reinforcement Learning in Game Applications
9. MAB for Financial Engineering
10. TD learning in HealthCare
11. Exploring Deep Reinforcement Learning methods
12. Deep Q learning Using Keras
13. Whats Next?

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 Q-learning to control robots.

You'll begin by learning the basic RL concepts, covering the agent-environment 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 multi-armed 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 real-world 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 well-versed with RL and have the skills you need to efficiently implement it with R.