Brief Introduction
This course will prepare you to participate in the reinforcement learning research community. You will also have the opportunity to learn from two of the foremost experts in this field of research, Profs. Charles Isbell and Michael Littman.Course Summary
Learn the foundations of reinforcement learning, a field of machine learning that focuses on teaching machines to make decisions based on rewards and punishments. This course covers topics such as Markov Decision Processes, dynamic programming, Monte Carlo methods, and deep reinforcement learning.Key Learning Points
- Understand the basics of reinforcement learning and how it differs from other machine learning techniques
- Learn about Markov Decision Processes and how they are used to model decision-making problems
- Explore different reinforcement learning algorithms, including dynamic programming, Monte Carlo methods, and deep reinforcement learning
- Apply reinforcement learning techniques to solve real-world problems in robotics, gaming, and more
Related Topics for further study
- Reinforcement Learning
- Machine Learning
- Markov Decision Processes
- Dynamic Programming
- Deep Reinforcement Learning
Learning Outcomes
- Understand the fundamentals of reinforcement learning
- Be able to apply reinforcement learning techniques to real-world problems
- Have a strong foundation for further study in the field
Prerequisites or good to have knowledge before taking this course
- Basic knowledge of probability and statistics
- Familiarity with Python
Course Difficulty Level
IntermediateCourse Format
- Online, self-paced
- Video lectures
- Hands-on exercises
Similar Courses
- Artificial Intelligence for Trading
- Deep Learning
- Self-Driving Car Engineer
Related Education Paths
Related Books
Requirements
- Before taking this course, you should have taken a graduate-level machine-learning course and should have had some exposure to reinforcement learning from a previous course or seminar in computer science. Additionally, you will be programming extensively in Java during this course. If you are not familiar with Java, we recommend you review Udacity's Object Oriented Programming in Java course materials to get up to speed beforehand. See the Technology Requirements for using Udacity.
Knowledge
- Instructor videosLearn by doing exercisesTaught by industry professionals
Summary of User Reviews
Read reviews and ratings of Reinforcement Learning on Udacity. From students who’ve taken this course, you can learn about how the course is rated, their experience and highlights.Key Aspect Users Liked About This Course
Many users found the course instructors to be knowledgeable and engaging.Pros from User Reviews
- The course content is well-structured and easy to follow.
- The assignments and projects are challenging and help reinforce the concepts.
- The course provides hands-on experience with real-world applications.
- The community and support system provided by Udacity is excellent.
- The course is well-suited for both beginners and experienced learners.
Cons from User Reviews
- Some users found the pace of the course to be too fast.
- There could be more interactive exercises and quizzes to break up the lectures.
- The course could benefit from more in-depth explanations of certain concepts.
- The course is quite technical and may require some prior knowledge in programming and mathematics.
- The course could benefit from more examples and case studies.
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