In this course, you will learn how to solve problems with large, high-dimensional, and potentially infinite state spaces. You will see that estimating value functions can be cast as a supervised learning problem---function approximation---allowing you to build agents that carefully balance generalization and discrimination in order to maximize reward. We will begin this journey by investigating how our policy evaluation or prediction methods like Monte Carlo and TD can be extended to the function approximation setting. You will learn about feature construction techniques for RL, and representation learning via neural networks and backprop. We conclude this course with a deep-dive into policy gradient methods; a way to learn policies directly without learning a value function. In this course you will solve two continuous-state control tasks and investigate the benefits of policy gradient methods in a continuous-action environment.
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来自PREDICTION AND CONTROL WITH FUNCTION APPROXIMATION的热门评论
This specialization is a gift to humanity. It should have been inscribed into the golden disc of the Voyager and shared with the aliens.
A great and interactive course to learn about using function approximation for control. Great way to learn DRL and its alternatives.
The course was really good one with quizzes to make us remember the important lesson items and well polished Assignments are given which i haven't seen before in coursera
more detailed explanation of some of the assignments and how state values are got with tile coding but overall a great experience!