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)

The AlphaGo project

AlphaGo is a software for the game of Go developed by Google DeepMind. It was the first software able to defeat a human champion in the game without a handicap and on a standard-sized goban (19 × 19).

DeepMind is a British company of artificial intelligence controlled by Alphabet. It was founded in 2011 as DeepMind Technologies and was acquired by Google in 2014.

According to David Silver, a researcher at DeepMind, the AlphaGo project was launched in 2014 to study how deep neural networks could be applied to the game of Go.

AlphaGo represented a significant advancement over pre-existing go-to game programs. Over 500 games played against other software, including Crazy Stone and Zen—AlphaGo (running on a single computer) has won all but one game—and running a series of similar matches, but turning on an AlphaGo cluster has won all 500 games...