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)

Handwritten digit recognition using an autoencoder

An autoencoder is a neural network whose purpose is to code its input in small size. The result obtained will then be used to reconstruct the input itself. Autoencoders are made up of the union of the following two subnets:

  • Encoder, which calculates the z = ϕ(x) function, given an x input, the encoder encodes it in a z variable, also called latent variable. The z variable usually has much smaller dimensions than x.
  • Decoder, which calculates the x' = ψ(z) function.

Since z is the code of x produced by the encoder, the decoder must decode it so that x' is similar to x.

The training of autoencoders is intended to minimize the mean squared error (MSE) between the input and the result.

MSE is the average squared difference between the output and targets. Lower values are indicative of better results. Zero means...