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)

Summary

In this chapter, we learned the basic concepts of artificial neural networks. We also learned how to apply neural network methods to our data, and how neural network algorithms work. We learned about the basic concepts that deep neural networks use to approximate reinforcement learning components.

Then, we looked at the basics of the Keras neural network model, as well as a practical example of the Keras neural network model. Then, we moved on to explore the Deep Q-learning concepts. The term "Deep Q-learning" refers to a reinforcement learning method that adopts a neural network as a function approximation. It therefore represents an evolution of the basic Q-learning method, as the state–action table is replaced by a neural network, with the aim of approximating the optimal value function. This network have the current state as input, and the corresponding...