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)

Neural network basic concepts

Artificial neural networks (ANN) are mathematical models for the simulation of typical human brain activities, such as image perception, pattern recognition, language understanding, sense–motor coordination, and so on. These models are composed of a system of nodes, equivalent to the neurons of a human brain, which are interconnected by weighted links, equivalent to the synapses between neurons, as shown in the following diagram:

The output of the network is iteratively changed from the link weights up to the convergence. The original data is provided to the input layer and the result of the network is returned from the output level. The input nodes represent the independent or predictor variables that are used to predict the dependent variables—that is, the output neurons.

Serial computers and their programs are very powerful tools...