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)

Playing the Board Game Go

Games have always been a phenomenon of human culture, where people manifest intelligence, interaction, and competition. But games are also an important theoretical paradigm in logic, artificial intelligence, computer science, linguistics, biology, and lately more and more in the social sciences and in psychology. Games, especially strategy games, offer reinforcement-learning algorithms an ideal and privileged environment for testing, as they can act as models for real problems. In this chapter, will learn how to use reinforcement-learning algorithms to address a problem in game theory.

The following are the topics covered:

  • Basic concepts of game theory
  • Game theory practical applications
  • AlphaGo DeepMind project
  • Monte Carlo Tree Search
  • Convolutional networks

At the end of the chapter, the reader will have learned basic concepts of game theory, how the...