Although introduced academically decades ago, the recent developments in the field of reinforcement learning have been phenomenal. Domains such as self-driving cars, natural language processing, healthcare industry, online recommender systems, and so on have already seen how RL-based AI agents can bring tremendous gains.
This course will help you get started with reinforcement learning first by establishing the motivation for this field and then covering all the essential topics, such as Markov Decision Processes, policy and rewards, model-free learning, temporal difference learning, and so on.
Each topic is accompanied by exercises and complementing analysis to help you gain practical and tangible coding skills.
By the end of this course, not only will you have gained the necessary understanding to implement RL in your projects but also implemented an actual Frozenlake project using the OpenAI Gym toolkit.
All resources and code files are placed here: https://github.com/PacktPublishing/Reinforcement-Learning-with-Python-E…
publication date
2021-02-26
what you will learn
Understand the motivation for reinforcement learning
Understand all the elements of a Markov Decision Process
Learn how to model uncertainty of the environments
Solve Markov Decision Processes
Implement temporal difference learning and Q-learning in Python
Execute the Frozenlake project using the OpenAI Gym toolkit
key features
Gain an understanding of all theoretical concepts related to reinforcement learning * Master learning models such as model-free learning, Q-learning, temporal difference learning * Model the uncertainty of the environment, environment stochastic policies, and environment value functions
approach
Through carefully designed modules, simple-to-understand theory, engaging hands-on exercises, and realistic implementations of Reinforcement Learning (RL) in projects, this course will help you master RL. This course covers the most advanced and up-to-date methods in RL.
audience
This course is designed for beginners in the field of data science and machine learning. Anyone who wants to learn RL and apply it in realistic projects would benefit from this course.
short description
This course begins with establishing the motivation for reinforcement learning and then progresses on to equipping you with all the necessary theory. Each section of the course helps you not only understand the fundamentals of RL but also gain necessary coding skills by taking you through exercises. By the end of the course, you will be able to complete a project using the OpenAI Gym toolkit.
subtitle
Master the fundamentals of reinforcement learning
keywords
Reinforcement Learning, Markov Decision Process, MDP, Q-learning, temporal difference learning, policy, rewards, OpenAI Gym
Product ISBN
9781801072274
