#### 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.
Preface
Free Chapter
Overview of Keras Reinforcement Learning
Simulating Random Walks
Optimal Portfolio Selection
Forecasting Stock Market Prices
Delivery Vehicle Routing Application
Continuous Balancing of a Rotating Mechanical System
Dynamic Modeling of a Segway as an Inverted Pendulum System
Robot Control System Using Deep Reinforcement Learning
Handwritten Digit Recognizer
Playing the Board Game Go
What's Next?
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# Summary

In this chapter, we looked at stochastic processes and their applications. We looked at starting a random walk model. Random walks are mathematical models that are used to describe a path given by a succession of random steps, which, depending on the system we want to describe, may have a certain number of degrees of freedom or direction. We have learned how to deal with one-dimensional random walks, and we have seen how to write a code for the simulation of a random walk in the Python language.

Then we were introduced to Markov chains. To understand this topic, you were briefly introduced to probability calculation. The a priori probability, joint probability, and conditional probability were all defined, with examples of their calculation. We then moved on to the definition of Markov chains. A Markov chain is a mathematical model of a random phenomenon that evolves over...