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.
