Book Image

Keras Reinforcement Learning Projects

By : Giuseppe Ciaburro
Book Image

Keras Reinforcement Learning Projects

By: Giuseppe Ciaburro

Overview of this book

Reinforcement learning has evolved a lot in the last couple of years and proven to be a successful technique in building smart and intelligent AI networks. Keras Reinforcement Learning Projects installs human-level performance into your applications using algorithms and techniques of reinforcement learning, coupled with Keras, a faster experimental library. The book begins with getting you up and running with the concepts of reinforcement learning using Keras. You’ll learn how to simulate a random walk using Markov chains and select the best portfolio using dynamic programming (DP) and Python. You’ll also explore projects such as forecasting stock prices using Monte Carlo methods, delivering vehicle routing application using Temporal Distance (TD) learning algorithms, and balancing a Rotating Mechanical System using Markov decision processes. Once you’ve understood the basics, you’ll move on to Modeling of a Segway, running a robot control system using deep reinforcement learning, and building a handwritten digit recognition model in Python using an image dataset. Finally, you’ll excel in playing the board game Go with the help of Q-Learning and reinforcement learning algorithms. By the end of this book, you’ll not only have developed hands-on training on concepts, algorithms, and techniques of reinforcement learning but also be all set to explore the world of AI.
Table of Contents (13 chapters)

What this book covers

Chapter 1, Overview of Keras Reinforcement Learning, will get you ready to enjoy reinforcement learning using Keras, looking at topics ranging from the basic concepts right to the building of models. By the end of this chapter, you will be ready to dive into working on real-world projects.

Chapter 2, Simulating Random Walks, will have you simulate a random walk using Markov chains through a Python code implementation.

Chapter 3, Optimal Portfolio Selection, explores how to select the optimal portfolio using dynamic programming through a Python code implementation.

Chapter 4, Forecasting Stock Market Prices, guides you in using the Monte Carlo methods to forecast stock market prices.

Chapter 5, Delivery Vehicle Routing Application, shows how to use Temporal Difference (TD) learning algorithms to manage warehouse operations through Python and the Keras library.

Chapter 6, Continuous Balancing of a Rotating Mechanical System, helps you to use deep reinforcement learning methods to balance a rotating mechanical system.

Chapter 7, Dynamic Modeling of a Segway as an Inverted Pendulum System, teaches you the basic concepts of Q-learning and how to use this technique to control a mechanical system.

Chapter 8, A Robot Control System Using Deep Reinforcement Learning, will confront you with the problem of robot navigation in simple maze-like environments where the robot has to rely on its on-board sensors to perform navigation tasks.

Chapter 9, Handwritten Digit Recognizer, shows how to set up a handwritten digit recognition model in Python using an image dataset.

Chapter 10, Playing the Board Game Go, explores how reinforcement learning algorithms were used to address a problem in game theory.

Chapter 11, What's Next?, gives a good understanding of the real-life challenges in building and deploying machine learning models, and explores additional resources and technologies that will help sharpen your machine learning skills.