Python Reinforcement Learning

Apply modern reinforcement learning and deep reinforcement learning methods using Python and its powerful libraries

Key Features

• Your entry point into the world of artificial intelligence using the power of Python
• An example-rich guide to master various RL and DRL algorithms
• Explore the power of modern Python libraries to gain confidence in building self-trained applications

Book Description

Reinforcement Learning (RL) is the trending and most promising branch of artificial intelligence. This Learning Path will help you master not only the basic reinforcement learning algorithms but also the advanced deep reinforcement learning algorithms.

The Learning Path starts with an introduction to RL followed by OpenAI Gym, and TensorFlow. You will then explore various RL algorithms, such as Markov Decision Process, Monte Carlo methods, and dynamic programming, including value and policy iteration. You'll also work on various datasets including image, text, and video. This example-rich guide will introduce you to deep RL algorithms, such as Dueling DQN, DRQN, A3C, PPO, and TRPO. You will gain experience in several domains, including gaming, image processing, and physical simulations. You'll explore TensorFlow and OpenAI Gym to implement algorithms that also predict stock prices, generate natural language, and even build other neural networks. You will also learn about imagination-augmented agents, learning from human preference, DQfD, HER, and many of the recent advancements in RL.

By the end of the Learning Path, you will have all the knowledge and experience needed to implement RL and deep RL in your projects, and you enter the world of artificial intelligence to solve various real-life problems.

This Learning Path includes content from the following Packt products:

• Hands-On Reinforcement Learning with Python by Sudharsan Ravichandiran
• Python Reinforcement Learning Projects by Sean Saito, Yang Wenzhuo, and Rajalingappaa Shanmugamani

What you will learn

• Train an agent to walk using OpenAI Gym and TensorFlow
• Solve multi-armed-bandit problems using various algorithms
• Build intelligent agents using the DRQN algorithm to play the Doom game
• Teach your agent to play Connect4 using AlphaGo Zero
• Defeat Atari arcade games using the value iteration method
• Discover how to deal with discrete and continuous action spaces in various environments

Who this book is for

If you're an ML/DL enthusiast interested in AI and want to explore RL and deep RL from scratch, this Learning Path is for you. Prior knowledge of linear algebra is expected.

Table of Contents

1. Introduction to Reinforcement Learning
2. Getting Started with OpenAI and TensorFlow
3. The Markov Decision Process and Dynamic Programming
4. Gaming with Monte Carlo Methods
5. Temporal Difference Learning
6. Multi-Armed Bandit Problem
7. Playing Atari Games
8. Atari Games with Deep Q Network
9. Playing Doom with a Deep Recurrent Q Network
10. The Asynchronous Advantage Actor Critic Network
11. Policy Gradients and Optimization
12. Balancing CartPole
13. Simulating Control Tasks
14. Building Virtual Worlds in Minecraft
15. Learning to Play Go
16. Creating a Chatbot
17. Generating a Deep Learning Image Classifier
18. Predicting Future Stock Prices
19. Capstone Project - Car Racing Using DQN
20. Looking Ahead

Sudharsan Ravichandiran is a data scientist, researcher, artificial intelligence enthusiast, and YouTuber (search for Sudharsan reinforcement learning). He completed his bachelors in information technology at Anna University. His area of research focuses on practical implementations of deep learning and reinforcement learning, which includes natural language processing and computer vision. He used to be a freelance web developer and designer and has designed award-winning websites. He is an open source contributor and loves answering questions on Stack Overflow.

Sean Saito is the youngest ever Machine Learning Developer at SAP and the first bachelor hired 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 Bachelor of Science degree (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.

Rajalingappaa Shanmugamani is currently working as an Engineering Manager for a Deep learning team at Kairos. Previously, he worked as a Senior Machine Learning Developer at SAP, Singapore and worked at various startups in developing machine learning products. He has a Masters from Indian Institute of Technology-Madras. He has published articles in peer-reviewed journals and conferences and submitted applications for several patents in the area of machine learning. In his spare time, he coaches programming and machine learning to school students and engineers.

Yang Wenzhuo works as a Data Scientist at SAP, Singapore. He got a bachelor's degree in computer science from Zhejiang University in 2011 and a Ph.D. in machine learning from the 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.