A practical introduction to creating agents with Python About This Book * Practical guide to implementing Q-learning, Markov models with Python and OpenAI *Explore the power of TensorFlow for building self-learning models *8 AI projects to gain confidence in building self-trained applications Who This Book Is For This book is for data analysts, data scientists, and machine learning professionals, who have working knowledge of machine learning techniques and are looking to build better performing, automated, and optimized deep learning model. Those who are aiming to work on self-learning model projects, will find this book invaluable. What You Will Learn * Train, and evaluate neural networks built using TensorFlow for RL *RL algorithms in Python and TensorFlow to solve the cartpole balancing *Create deep reinforcement learning algorithm to play ATARI games *Deploy RL algorithms using OpenAI Universe *Develop an agent to simulate the email reply for browser tasks *Implement basic actor-critic algorithms for continuous control *Apply advanced deep RL algorithms to games like Minecraft *Explore the algorithms introduced by Google Brain In Detail Reinforcement learning is hot. It is the next big leap in the artificial intelligence domain because it is unsupervised, optimized, fast, and is self-learning. This book aims to take you to various aspects and methodologies in reinforcement learning with the help of insightful projects. You will learn important concepts in reinforcement learning such as Q-learning, Markov models, Monte-Carlo process, deep reinforcement learning, and more. The insightful projects will work with various data sets such as numerical, text, video, and audio. You can see projects from areas such as gaming, image processing, audio processing, recommendation systems, and more. Use TensorFlow and OpenAI Gym to implement a deep learning RL agent that can play an ATARI game. Learn how to tune and configure RL algorithms and parameters by building agents for different kinds of games. You will learn how to build self-learning models that will not only uncover layers of data but also reason and make decisions. By the end of this book, you will have created 8 real-world projects that explore reinforcement learning and will have hands-on experience with real data and artificial intelligence problems.