Reinforcement Learning Spring 2022

Graduate course, National Kaohsiung University of Science and Technology, 2022

Course Description

Reinforcement Learning (RL) is applied to many applications such as dialog system, robotics, and AlphaGo. RL mimics the learning behaviors of human beings and endows the system with the ability to learn through the trial-and-error method. The course will introduce the foundamental theories and the advanced theories of RL. We will start from Markolv decision process to deep RL. Moreover, we will introduce the applications of RL. More specifically, we focus our applications on dialog systems. After this course, students will have foundamental concepts to do RL research.

Lecture 0: AidIR: An Interactive Dialog System to Aid Disease Information Retrieval

Lecture 1: Introduction to Reinforcement Learning

Lecture 2: Markov Decision Processes

Lecture 3: Planning by Dynamic Programming

Lecture 4: Model-Free Prediction

Lecture 5: Model-Free Control

Lecture 6: Neural Network and Backpropagation

Lecture 7: Value Function Approximation

Lecture 8: Policy Gradient

Lecture 9: Actor-Critic Algorithm

Lecture 10: Variational Inference and Generative Models


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