In this final course, you will put together your knowledge from Courses 1, 2 and 3 to implement a complete RL solution to a problem. This capstone will let you see how each component---problem formulation, algorithm selection, parameter selection and representation design---fits together into a complete solution, and how to make appropriate choices when deploying RL in the real world. This project will require you to implement both the environment to stimulate your problem, and a control agent with Neural Network function approximation. In addition, you will conduct a scientific study of your learning system to develop your ability to assess the robustness of RL agents. To use RL in the real world, it is critical to (a) appropriately formalize the problem as an MDP, (b) select appropriate algorithms, (c ) identify what choices in your implementation will have large impacts on performance and (d) validate the expected behaviour of your algorithms. This capstone is valuable for anyone who is planning on using RL to solve real problems.
- 5 stars77.35%
- 4 stars16.46%
- 3 stars5.14%
- 2 stars0.68%
- 1 star0.34%
A COMPLETE REINFORCEMENT LEARNING SYSTEM (CAPSTONE) からの人気レビュー
It may have been useful to provide less guidance to the students to make sure they develop the required skills. Overall, it was a nice exercise to implement a TD(0) network.
The course is applicative in real world projects. I think it is a very good choice for any one that is interested to learn how to apply reinforcement learning.
Good project as a capstone. Wish there would have been more work needed from our side of things in terms of coding, but very solid final course for RL.
This course changed my life! It was so good and I learned so much. I can't believe I'm now an astronaut. Next mission: go to Mars!