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Full Description
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.
Contents
Chapter 1: Introduction to Reinforcement Learning.- Chapter 2: Reinforcement Learning Algorithms.- Chapter 3: Q Learning.- Chapter 4: Reinforcement Learning Based Market Making.- Chapter 5: Reinforcement Learning for Video Games.
