Python Reinforcement Learning

Apply modern reinforcement learning and deep reinforcement learning methods using Python and its powerful librariesKey FeaturesYour entry point into the world of artificial intelligence using the power of PythonAn example-rich guide to master various RL and DRL algorithmsExplore the power of modern Python libraries to gain confidence in building self-trained applicationsBook DescriptionReinforcement 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 RavichandiranPython Reinforcement Learning Projects by Sean Saito Yang Wenzhuo and Rajalingappaa ShanmugamaniWhat you will learnTrain an agent to walk using OpenAI Gym and TensorFlowSolve multi-armed-bandit problems using various algorithmsBuild intelligent agents using the DRQN algorithm to play the Doom gameTeach your agent to play Connect4 using AlphaGo ZeroDefeat Atari arcade games using the value iteration methodDiscover how to deal with discrete and continuous action spaces in various environmentsWho this book is forIf 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. About the Author Sudharsan Ravichandiran is a data scientist researcher Artificial Intelligence enthusiast and YouTuber (search for "Sudharsan reinforcement learning"). He completed his Bachelor's in Information Technology at Anna University. His area of research focuses on practical implementations of deep learning and reinforcement learning including Natural Language Processing and computer vision. He is an open source contributor and loves answering questions on Stack Overflow. He also authored a best-seller Hands-On Reinforcement Learning with Python published by Packt Publishing.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