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学生对 阿尔伯塔大学 提供的 A Complete Reinforcement Learning System (Capstone) 的评价和反馈

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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. To be successful in this course, you will need to have completed Courses 1, 2, and 3 of this Specialization or the equivalent. By the end of this course, you will be able to: Complete an RL solution to a problem, starting from problem formulation, appropriate algorithm selection and implementation and empirical study into the effectiveness of the solution....



Apr 27, 2020

This is the final chapter. It is one of the easiest and it was fun doing that lunar landing project. This specialisation is the best for a person taking baby steps in the reinforcement learning.


Feb 26, 2020

Great course for learning the fundamentals. I liked that it tied into function approximation for deep reinforcement learning. The text book made the fundamental concepts more clear.


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