The virtual course "Prediction and Control with Approximation of Functions - Virtual Course - Coursera", is a course with different contents and that offers video classes of . Explore its essential features, and click the orange button for detailed information on the Coursera e-Learning platform.
In this course, you will learn to solve problems with large, high-dimensional, and potentially infinite state spaces. He will see that value function estimation can be turned into a supervised learning problem (function approximation), allowing him to create agents that carefully balance generalization and discrimination to maximize payoff. We will start this journey by investigating how our evaluation of policies or prediction methods like Monte Carlo and TD can be extended to the approximation configuration of functions. You will learn about feature construction techniques for RL and representation learning through 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. Prerequisites: This course builds heavily on the foundations of Courses 1 and 2, and students must have completed them prior to beginning this course. Students should also be comfortable with probabilities and expectations, basic linear algebra, basic calculus, Python 3.0 (at least 1 year), and implementing algorithms from pseudocode. By the end of this course, you will be able to: -Understand how to use supervised learning approaches to approximate value functions -Understand objectives for prediction (value estimation) under function approximation -Implement TD with function approximation (state aggregation),
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