CSCE-642
Reinforcement Learning

Spring 2025

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Participation

Please ensure that you meet the course's expected background knowledge. If you are unsure, contact the course staff for clarification. Registered students are required to participate in:

  • Online quizzes available on the course's Canvas website.
  • Programming assignments.
  • Original research as part of a course project.

General questions, comments, and discussions should be posted on Campuswire (enrollment code: 9697). Please avoid sending general questions to the course staff via email, as such queries will be redirected to the Campuswire discussion board. You are encouraged to assist fellow students with posted issues, as extra credit will be awarded to exceptionally helpful participants.


Text Books

  • Reinforcement Learning: An Introduction
  • Reinforcement Learning: State-of-the-Art
  • Artificial Intelligence: A Modern Approach
  • Deep Learning

Lectures

Lecture #SlidesLecture
1Introduction.pptxRecording
2Multiarmed Bandit.pptxRecording
3MDPs.pptxRecording
4Monte-Carlo Methods.pptxRecording
5Temporal Difference.pptxRecording
6Bootstrapping.pptxRecording
7Model-based RL.pptxRecording
8Prediction with Approximation.pptxRecording
9DNN Approximation.pptxRecording
10Control with Approximation.pptxRecording
11Eligibility Traces.pptxRecording
12Deep Q-Learning.pptxRecording
13Policy Gradien.pptxRecording
14Actor Critic.pptxRecording
15Trust Regions.pptxRecording
16Soft Actor-Critic.pptxRecording
17Transfer Learning.pptxRecording
18Immitation Learning.pptxRecording
19Derivative Free.pptxRecording
20Curriculum Learning.pptxRecording

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