Prediction and Control with Function Approximation(으)로 돌아가기
앨버타 대학교의 Prediction and Control with Function Approximation 학습자 리뷰 및 피드백
강좌 소개
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. This course strongly builds on the fundamentals of Courses 1 and 2.
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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