Applied Reinforcement Learning with Python: With OpenAI Gym, Tensorflow, and Keras

Delve into the world of reinforcement learning algorithms and apply them to different use-cases via Python. This book covers important topics such as policy gradients and Q learning, and utilizes frameworks such as Tensorflow, Keras, and OpenAI Gym. Applied Reinforcement Learning with Python introduces you to the theory behind reinforcement learning (RL) algorithms and the code that will be used to implement them. You will take a guided tour through features of OpenAI Gym, from utilizing standard libraries to creating your own environments, then discover how to frame reinforcement learning problems so you can research, develop, and deploy RL-based solutions. What You'll Learn Implement reinforcement learning with Python Work with AI frameworks such as OpenAI Gym, Tensorflow, and Keras Deploy and train reinforcement learning-based solutions via cloud resources Apply practical applications of reinforcement learning Who This Book Is For Data scientists, machine learning engineers and software engineers familiar with machine learning and deep learning concepts.;Intro; Table of Contents; About the Author; About the Technical Reviewer; Acknowledgments; Introduction; Chapter 1: Introduction to Reinforcement Learning; History of Reinforcement Learning; MDPs and their Relation to Reinforcement Learning; Reinforcement Learning Algorithms and RL Frameworks; Q Learning; Actor-Critic Models; Applications of Reinforcement Learning; Classic Control Problems; Super Mario Bros.; Doom; Reinforcement-Based Marketing Making; Sonic the Hedgehog; Conclusion; Chapter 2: Reinforcement Learning Algorithms; OpenAI Gym; Policy-Based Learning