Table of Contents

Getting started with reinforcement learning and PyTorch

Markov Decision Process and Dynamic Programming

Monte Carlo Methods for making numerical estimations

Temporal Difference and Q-Learning

Solving Multi Armed Bandit problems

Scaling up Learning with Function Approximation

Deep Q-Networks in Action

Implementing Policy Gradients and Policy Optimization

Capstone Project: Playing Flappy Bird with DQN

About the Author

Yuxi (Hayden) Liu is an experienced data scientist who's focused on developing machine learning and deep learning models and systems. He has worked in a variety of data-driven domains and has applied his expertise in reinforcement learning to computational. He is an education enthusiast and the author of a series of ML books. His first book, Python Machine Learning By Example, was a #1 bestseller on Amazon India in 2017 and 2018. His other books include R Deep Learning Projects and Hands-On Deep Learning Architectures with Python published by Packt. He also published five first-authored IEEE transaction and conference papers during his master's research at the University of Toronto.