Practical Deep Reinforcement Learning with Python

Introducing Practical Smart Agents Development using Python PyTorch and TensorFlowDescription Reinforcement learning is a fascinating branch of AI that differs from standard machine learning in several ways. Adaptation and learning in an unpredictable environment is the part of this project. There are numerous real-world applications for reinforcement learning these days including medical gambling human imitation activity and robotics.. This book introduces readers to reinforcement learning from a pragmatic point of view. The book does involve mathematics but it does not attempt to overburden the reader who is a beginner in the field of reinforcement learning.. The book brings a lot of innovative methods to the readers attention in much practical learning including Monte-Carlo Deep Q-Learning Policy Gradient and Actor-Critical methods. While you understand these techniques in detail the book also provides a real implementation of these methods and techniques using the power of TensorFlow and PyTorch. What you will learn ● Familiarize yourself with the fundamentals of Reinforcement Learning and Deep Reinforcement Learning. ● Make use of Python and Gym framework to model an external environment. ● Apply classical Q-learning Monte Carlo Policy Gradient and Thompson sampling techniques. ● Explore TensorFlow and PyTorch to practice the fundamentals of deep reinforcement learning. ● Design a smart agent for a particular problem using a specific technique. Who this book is for This book is for machine learning engineers deep learning fanatics AI software developers data scientists and other data professionals eager to learn and apply Reinforcement Learning to ongoing projects. No specialized knowledge of machine learning is necessary; however proficiency in Python is desired.