num_pages: 532
description: Get hands-on experience in creating state-of-the-art reinforcement learning agents using TensorFlow and RLlib to solve complex real-world business and industry problems with the help of expert tips and best practices Reinforcement learning (RL) is a field of artificial intelligence (AI) used for creating self-learning autonomous agents. Building on a strong theoretical foundation this book takes a practical approach and uses examples inspired by real-world industry problems to teach you about state-of-the-art RL. Starting with bandit problems Markov decision processes and dynamic programming the book provides an in-depth review of the classical RL techniques such as Monte Carlo methods and temporal-difference learning. After that you will learn about deep Q-learning policy gradient algorithms actor-critic methods model-based methods and multi-agent reinforcement learning. Then you'll be introduced to some of the key approaches behind the most successful RL implementations such as domain randomization and curiosity-driven learning. As you advance youll explore many novel algorithms with advanced implementations using modern Python libraries such as TensorFlow and Rays RLlib package. Youll also find out how to implement RL in areas such as robotics supply chain management marketing finance smart cities and cybersecurity while assessing the trade-offs between different approaches and avoiding common pitfalls. By the end of this book youll have mastered how to train and deploy your own RL agents for solving RL problems. This book is for expert machine learning practitioners and researchers looking to focus on hands-on reinforcement learning with Python by implementing advanced deep reinforcement learning concepts in real-world projects. Reinforcement learning experts who want to advance their knowledge to tackle large-scale and complex sequential decision-making problems will also find this book useful. Working knowledge of Python programming and deep learning along with prior experience in reinforcement learning is required.
language: English
title: Mastering Reinforcement Learning with Python: Build next-generation, self-learning models using reinforcement learning techniques and best practices
publisher: Packt Publishing