Zurück zu Prediction and Control with Function Approximation

4.9

10 Bewertungen

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2 Bewertungen

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.
Prerequisites: This course strongly builds on the fundamentals of Courses 1 and 2, and learners should have completed these before starting this course. Learners should also be comfortable with probabilities & 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), on an environment with an infinite state space (continuous state space)
-Understand fixed basis and neural network approaches to feature construction
-Implement TD with neural network function approximation in a continuous state environment
-Understand new difficulties in exploration when moving to function approximation
-Contrast discounted problem formulations for control versus an average reward problem formulation
-Implement expected Sarsa and Q-learning with function approximation on a continuous state control task
-Understand objectives for directly estimating policies (policy gradient objectives)
-Implement a policy gradient method (called Actor-Critic) on a discrete state environment...

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von Chang, W C

•Oct 12, 2019

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

But the assignment sometime make me frustrated, it is not easy to address the bug when just receive a feedback "Learning curve is far from expected answer"

von Luiz C

•Oct 03, 2019

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 Course

2/ 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