Python Reinforcement Learning Projects: Eight hands-on projects exploring reinforcement learning algorithms using TensorFlow
By 作者: Sean Saito – Yang Wenzhuo – Rajalingappaa Shanmugamani
ISBN-10 书号: 1788991613
ISBN-13 书号: 9781788991612
Release Finelybook 出版日期: 2018-09-29
pages 页数: (296 )

$44.99

Book Description to Finelybook sorting

Reinforcement learning is one of the most exciting and rapidly growing fields in machine learning. This is due to the many novel algorithms developed and incredible results published in recent years.
In this book, you will learn about the core concepts of RL including Q-learning, policy gradients, Monte Carlo processes, and several deep reinforcement learning algorithms. As you make your way through the book, you’ll work on projects with datasets of various modalities including image, text, and video. You will gain experience in several domains, including gaming, image processing, and physical simulations. You’ll explore technologies such as TensorFlow and OpenAI Gym to implement deep learning reinforcement learning algorithms that also predict stock prices, generate natural language, and even build other neural networks.
By the end of this book, you will have hands-on experience with eight reinforcement learning projects, each addressing different topics and/or algorithms. We hope these practical exercises will provide you with better intuition and insight about the field of reinforcement learning and how to apply its algorithms to various problems in real life.
Contents
1: UP AND RUNNING WITH REINFORCEMENT LEARNING
2: BALANCING CARTPOLE
3: PLAYING ATARI GAMES
4: SIMULATING CONTROL TASKS
5: BUILDING VIRTUAL WORLDS IN MINECRAFT
6: LEARNING TO PLAY GO
7: CREATING A CHATBOT
8: GENERATING A DEEP LEARNING IMAGE CLASSIFIER
9: PREDICTING FUTURE STOCK PRICES
10: LOOKING AHEAD
What You Will Learn
Train and evaluate neural networks built using TensorFlow for RL
Use RL algorithms in Python and TensorFlow to solve CartPole balancing
Create deep reinforcement learning algorithms to play Atari games
Deploy RL algorithms using OpenAI Universe
Develop an agent to chat with humans
Implement basic actor-critic algorithms for continuous control
Apply advanced deep RL algorithms to games such as Minecraft
Autogenerate an image classifier using RL
Authors
Sean Saito
Sean Saito is the youngest ever Machine Learning Developer at SAP and the first bachelor hire for the position. He currently researches and develops machine learning algorithms that automate financial processes. He graduated from Yale-NUS College in 2017 with a Bachelors of Science (with Honours), where he explored unsupervised feature extraction for his thesis. Having a profound interest in hackathons, Sean represented Singapore during Data Science Game 2016, the largest student data science competition. Before attending university in Singapore, Sean grew up in Tokyo, Los Angeles, and Boston.
Yang Wenzhuo
Yang Wenzhuo is working as a Data Scientist at SAP, Singapore. He got a bachelor’s degree in computer science from Zhejiang University in 2011 and a PhD degree in machine learning from National University of Singapore in 2016. His research focuses on optimization in machine learning and deep reinforcement learning. He has published papers on top machine learning/computer vision conferences including ICML and CVPR, and operations research journals including Mathematical Programming.

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