Fundamentals of Reinforcement Learning
Learn the fundamentals of Reinforcement Learning and how to apply them to real-world problems. This course introduces you to the key concepts of Reinforcement Learning, underlying classic and modern algorithms in RL. Gain the skills to start using RL for real problems and make intelligent decisions. ▼
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Course Feature
Cost:
Free
Provider:
Coursera
Certificate:
Paid Certification
Language:
English
Start Date:
4th Sep, 2023
Course Overview
❗The content presented here is sourced directly from Coursera platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.
Updated in [June 30th, 2023]
The Fundamentals of Reinforcement Learning course provides an introduction to the field of Reinforcement Learning (RL). Students will learn how to formalize problems as Markov Decision Processes, understand basic exploration methods and the exploration/exploitation tradeoff, understand value functions as a general-purpose tool for optimal decision-making, and know how to implement dynamic programming as an efficient solution approach to an industrial control problem. Upon completion of the course, students will be able to start using RL for real problems, where they have or can specify the Markov Decision Process. This is the first course of the Reinforcement Learning Specialization.
[Applications]
Upon completion of this course, learners can apply the fundamentals of Reinforcement Learning to real-world problems. Learners can use the knowledge gained to implement dynamic programming as an efficient solution approach to an industrial control problem. Additionally, learners can use the concepts of value functions to make optimal decisions.
[Career Paths]
One job position path that is recommended for learners of this course is a Reinforcement Learning Engineer. A Reinforcement Learning Engineer is responsible for designing, developing, and deploying reinforcement learning algorithms to solve complex problems. They must have a strong understanding of the fundamentals of reinforcement learning, including Markov Decision Processes, exploration methods, and value functions. They must also be able to implement dynamic programming solutions to industrial control problems.
The development trend for Reinforcement Learning Engineers is very positive. As more and more companies are interested in interactive agents and intelligent decision-making, the demand for Reinforcement Learning Engineers is expected to increase. Additionally, as the field of reinforcement learning continues to evolve, Reinforcement Learning Engineers will need to stay up to date with the latest advancements in the field in order to remain competitive.
[Education Paths]
The recommended educational path for learners of this course is to pursue a degree in Artificial Intelligence (AI) or Machine Learning (ML). This degree will provide learners with a comprehensive understanding of the fundamentals of AI and ML, as well as the ability to apply these concepts to real-world problems. The degree will also cover topics such as natural language processing, computer vision, robotics, and deep learning.
The development trend of AI and ML degrees is to focus on the practical application of the concepts. This means that learners will be able to apply their knowledge to real-world problems and develop solutions that can be used in industry. Additionally, the degree will focus on the ethical implications of AI and ML, as well as the potential for misuse. This will ensure that learners are aware of the potential risks and benefits of AI and ML, and can make informed decisions when using these technologies.
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