Random walks are a mathematical model that is used to describe a path that is given by a succession of random steps, which, depending on the system that we want to describe, may have a certain number of degrees of freedom or direction. The term random walk was introduced by Karl Pearson in 1905. In a random walk, each step has a random direction and possibly also a random dimension. It represents a theoretical model to describe any random process through the evolution of known quantities that follow a precise statistical distribution. Physically speaking, the path that we are going to draw over time will not necessarily describe a real motion, but rather indicate more generally the evolution of features over time. This means that random walks find applications in physics, chemistry, and biology, but also in other fields, such as computer science, economics, and sociology...
Keras Reinforcement Learning Projects
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Keras Reinforcement Learning Projects
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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.
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Keras Reinforcement Learning Projects
Table of Contents (13 chapters)
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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