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.