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 have addressed the basic concepts of the optimization techniques. To start, we learned the essential contents underlying the DP. With DP, we subdivide an optimization problem into simpler subproblems. We then proceed to calculate the solutions of all possible subproblems, and starting from subsolutions, we obtain new subsolutions, and carry on until we solve the original problem.

Then, we looked at the difference between recursion and memoization. In DP, this does not happen: we memorize the solution of these subproblems so that we do not have to solve them again; this is called memoization. The idea behind this method is to calculate solutions to subproblems once and store the solutions in a table so that they can be reused (repeatedly) later. To better understand this technique, we looked at a practical case: the calculation of the factorial of a...