The selection of an optimal portfolio is a typical decision problem, and as such, its solution consists of the following elements: the identification of a set of alternatives, using selection criteria to sort through the different possibilities, and finally the solution of the problem. Dynamic Programming (DP) represents a set of algorithms that can be used to calculate an optimal policy given a perfect model of the environment in the form of a MarkovDecision Process (MDP). The DP methods update the estimates of the values of the states—based on the estimates of the values of the successor states—or update the estimates on the basis of past estimates. In DP, an optimization problem is decomposed into simpler subproblems, and the solution for each subproblem is stored so that each subproblem is solved only once. In this chapter, we will...
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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