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)

Deep autoencoder Q-learning

As we saw in previous chapters, reinforcement learning demonstrates insufficient adaptability to high-dimensional input data. This problem is overcome by using low-dimensional characteristics vectors to represent high-dimensional input. However, finding useful vectors of features can be complicated, as it requires a good understanding of the problem.

One way to change the dimensionality of data is the autoencoder. Autoencoders are artificial neural networks with a hidden layer, which has the desired dimensionality of the input data; both input and output levels have the same amount of units. In these models, the network is trained to reproduce the input values ​​in the output level. As we saw in the previous section, the autoencoder learns two functions: an encoder function and a decoder function.

During reinforcement learning, the amount...