In the examples of the previous chapters, the estimates of the value function were made using a table, in which each box represents a state or a state–action pair. The use of a table to represent the value function allows the creation of simple algorithms and, if the environmental conditions are Markovian, allows to accurately estimate the value function because it assigns the expected return learned during policy iterations to every possible configuration from the environment. The use of the table, however, also leads to limitations; in fact, these methods are applicable only to environments with a reduced number of states and actions. The problem is not limited to the large amount of memory required to store the table, but also to the large amount of data and time required to estimate each state–action pair accurately. In other words...
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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