The **Vehicle Routing**** Problem** (**VRP**) is a typical distribution and transport problem, which consists of optimizing the use of a set of vehicles with limited capacity to pick up and deliver goods or people to geographically distributed stations. Managing these operations in the best possible way can significantly reduce costs. **Temporal difference** (**TD**) learning algorithms are based on reducing the differences between estimates made by the agent at different times. It is a combination of the ideas of the **Monte Carlo** (**MC**) method and **Dynamic Programming** (**DP**). It can learn directly from raw data, without a model of the dynamics of the environment (such as MC). Update estimates are based in part on other learned estimates, without waiting for the final result (bootstrap, like in DP). In this chapter, we will learn how to use TD learning algorithms to...

#### 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.

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

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