Prediction and Control with Function Approximation

Course is great! Maybe some slides would be helpful not to forget.

The material is very good. But this course needs better instructors/ method of teaching. The book is also written in an unnecessarily technical way filled with jargon. explanations are not clear, simple stuff is presented in a very complicated manner for no obvious way.

Almost perfect, except two ~minor objections:1/ the learning content between the 4 weeks is quite unbalanced. The initial weeks of the course are well sized, whereas week #3 and week #4 feel a touch light. It feels like the Instructors rushed to make the Course available online, and didn't have time to put as much content as they wished in the last weeks of the Course2/ there are too many typos in some notebooks (specifically notebook of week #3). It gives the impression it was made in a rush, and nobody read over it again. Besides there seems to currently be some issue with this assignment

I had a much better experience with the autograder than in course 2.

Simply the best course on this topic.

Great course and specialization. The teachers are great, the material well presented and balanced. I strongly recommend this course to anyone interested in the field of Reinforcement Learning. For maximum chance of success I suggest following all 3 courses in succession and investing the necessary amount of time to read the textbook chapters as specified at the beginning of each week.Looking forward to completing the capstone project now!

Great course. Slightly more complex than courses 1 and 2, but a huge improvement in terms of applicability to real-world situations.

Its got a great variety of very applicable examples, use cases, and assignments. May be tough if people don't quite understand how neural networks work, so I suggest having a basic understanding of NN for parts of this course.

This, the third in an exceptionally well-paced series of four courses on Reinforcement Learning, extends the scope of the subject to include parameterized functions (i.e., neural networks). The section on tiling methods is especially interesting. The course is taught under the auspices of professors who, quite literally, wrote the book on reinforcement learning, and includes several video lectures by leading practitioners and theorists in the field. The final programming assignment, in particular, made me feel like I did when I wrote my first computer program that actually did what it was supposed to way back when -- delight and amazement.

The course presentation is wonderful. I can't stop after I watch the first video.

Great Learning, the best part was the Actor-Critic algorithm for a small pendulum swing task all from stratch using RLGLue library. Love to learn how experimentation in RL works.