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Keras Reinforcement Learning Projects

By : Giuseppe Ciaburro

2.3 (3)

Buy this Book

Keras Reinforcement Learning Projects

2.3 (3)

By: Giuseppe Ciaburro

Buy this Book

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

Who this book is for

What this book covers

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Free Chapter

Overview of Keras Reinforcement Learning

Basic concepts of machine learning

Getting started with reinforcement learning

Reinforcement learning algorithms

Summary

Simulating Random Walks

Random walks

Markov chains

Summary

Optimal Portfolio Selection

Dynamic Programming

Optimizing a financial portfolio

Solving the knapsack problem using Dynamic Programming

Summary

Forecasting Stock Market Prices

Monte Carlo methods

Amazon stock price prediction using Python

Summary

Delivery Vehicle Routing Application

Temporal difference learning

Basics of graph theory

Finding the shortest path

The Vehicle Routing Problem

Summary

Continuous Balancing of a Rotating Mechanical System

Neural network basic concepts

Classifying breast cancer using the neural network

Deep reinforcement learning

Continuous control with deep reinforcement learning

Summary

Dynamic Modeling of a Segway as an Inverted Pendulum System

How Segways work

OpenAI Gym

The CartPole system

Summary

Robot Control System Using Deep Reinforcement Learning

Robot control

The FrozenLake environment

Summary

Handwritten Digit Recognizer

Handwritten digit recognition

Handwritten digit recognition using an autoencoder

Deep autoencoder Q-learning

Summary

Playing the Board Game Go

Game theory

The Go game

The AlphaGo project

Summary

What's Next?

Reinforcement-learning applications in real life

Next steps for reinforcement learning

Summary

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Playing the Board Game Go

Games have always been a phenomenon of human culture, where people manifest intelligence, interaction, and competition. But games are also an important theoretical paradigm in logic, artificial intelligence, computer science, linguistics, biology, and lately more and more in the social sciences and in psychology. Games, especially strategy games, offer reinforcement-learning algorithms an ideal and privileged environment for testing, as they can act as models for real problems. In this chapter, will learn how to use reinforcement-learning algorithms to address a problem in game theory.

The following are the topics covered: