Ciaburro / Ravichandiran / Ramamoorthy Keras Reinforcement Learning Projects

9 projects exploring popular reinforcement learning techniques to build self-learning agents
1. Auflage 2024
ISBN: 978-1-78934-797-5
Verlag: De Gruyter
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 288 Seiten

A practical guide to mastering reinforcement learning algorithms using KerasKey Features - Build projects across robotics, gaming, and finance fields, putting reinforcement learning (RL) into action
- Get to grips with Keras and practice on real-world unstructured datasets
- Uncover advanced deep learning algorithms such as Monte Carlo, Markov Decision, and Q-learning
Book DescriptionReinforcement 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. What you will learn - Practice the Markov decision process in prediction and betting evaluations
- Implement Monte Carlo methods to forecast environment behaviors
- Explore TD learning algorithms to manage warehouse operations
- Construct a Deep Q-Network using Python and Keras to control robot movements
- Apply reinforcement concepts to build a handwritten digit recognition model using an image dataset
- Address a game theory problem using Q-Learning and OpenAI Gym
Who this book is forKeras Reinforcement Learning Projects is for you if you are data scientist, machine learning developer, or AI engineer who wants to understand the fundamentals of reinforcement learning by developing practical projects. Sound knowledge of machine learning and basic familiarity with Keras is useful to get the most out of this book

Autoren/Hrsg.

Ciaburro, Giuseppe

Ravichandiran, Sudharsan

Ramamoorthy, Suriyadeepan

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Theoretische Informatik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Neuronale Netzwerke

Inhaltsverzeichnis

Table of Contents - Overview of Keras Reinforcement Learning
- Simulating random walks
- Optimal Portfolio Selection
- Forecasting stock market prices
- Delivery Vehicle Routing Application
- Prediction and Betting Evaluations of coin flips using Markov decision processes
- Build an optimized vending machine using Dynamic Programming
- Robot control system using Deep Reinforcement Learning
- Handwritten Digit Recognizer
- Playing the board game Go

- What is next?

Über Autor(innen)

Ciaburro Giuseppe :

Giuseppe Ciaburro holds a PhD and two master's degrees. He works at the Built Environment Control Laboratory - Università degli Studi della Campania "Luigi Vanvitelli". He has over 25 years of work experience in programming, first in the field of combustion and then in acoustics and noise control. His core programming knowledge is in MATLAB, Python and R. As an expert in AI applications to acoustics and noise control problems, Giuseppe has wide experience in researching and teaching. He has several publications to his credit: monographs, scientific journals, and thematic conferences. He was recently included in the world's top 2% scientists list by Stanford University (2022).