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)

Overview of Keras Reinforcement Learning

Nowadays, most computers are based on a symbolic elaboration, that is, the problem is first encoded in a set of variables and then processed using an explicit algorithm that, for each possible input of the problem, offers an adequate output. However, there are problems in which resolution with an explicit algorithm is inefficient or even unnatural, for example with a speech recognizer; tackling this kind of problem with the classic approach is inefficient. This and other similar problems, such as autonomous navigation of a robot or voice assistance in performing an operation, are part of a very diverse set of problems that can be addressed directly through solutions based on reinforcement learning.

Reinforcement learning is a very exciting part of machine learning, used in applications ranging from autonomous cars to playing games. Reinforcement learning aims to create algorithms that can learn and adapt to environmental changes. To do this, we use external feedback signals (reward signals) generated by the environment according to the choices made by the algorithm. A correct choice will result in a reward, while an incorrect choice will lead to a penalization of the system. All of this is in order to achieve the best result obtainable.

The topics covered in this chapter are the following:

  • An overview of machine learning
  • Reinforcement learning
  • Markov Decision Process (MDP)
  • Temporal difference (TD) learning
  • Q-learning
  • Deep Q-learning networks

At the end of the chapter, you will be fully introduced to the power of reinforcement learning and will learn the different approaches to this technique. Several reinforcement learning methods will be covered.