Keras Reinforcement Learning Projects: 9 projects exploring popular reinforcement learning techniques to build self-learning agents
Authors: Giuseppe Ciaburro
ISBN-10: 1789342090
ISBN-13: 9781789342093
Released: 2018-09-29
Print Length 页数: 288 pages
Book Description
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.
Contents
1: OVERVIEW OF KERAS REINFORCEMENT LEARNING
2: SIMULATING RANDOM WALKS
3: OPTIMAL PORTFOLIO SELECTION
4: FORECASTING STOCK MARKET PRICES
5: DELIVERY VEHICLE ROUTING APPLICATION
6: CONTINUOUS BALANCING OF A ROTATING MECHANICAL SYSTEM
7: DYNAMIC MODELING OF A SEGWAY AS AN INVERTED PENDULUM SYSTEM
8: ROBOT CONTROL SYSTEM USING DEEP REINFORCEMENT LEARNING
9: HANDWRITTEN DIGIT RECOGNIZER
10: PLAYING THE BOARD GAME GO
11: WHAT’S NEXT?
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
Authors
Giuseppe Ciaburro
Giuseppe Ciaburro holds a PhD in environmental technical physics and two master’s degrees. His research focuses on machine learning applications in the study of urban sound environments. He works at Built Environment Control Laboratory – Università degli Studi della Campania Luigi Vanvitelli (Italy). He has over 15 years of work experience in programming (in Python,R,and MATLAB),first in the field of combustion and then in acoustics and noise control. He has several publications to his credit.
