Lecture Materials

Lecture Materials

Lecture materials for this course are given below.
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Topic   Course Materials  
Introduction to Reinforcement Learning
  1. Lecture 1 Draft Slides [Post class version]
  2. Additional Materials:
Tabular MDP planning
  1. Lecture 2 Slides (pre-class) [Post class, annotated]
  2. Additional Materials:
    • SB (Sutton and Barto) Chp 3, 4.1-4.4
Tabular RL policy evaluation
  1. Lecture 3 Slides (pre-class) [Post class, with annotations]
  2. Additional Materials:
    • SB (Sutton and Barto) Chp 5.1, 5.5, 6.1-6.3
Q-learning
  1. Lecture 4 Slides (preclass) (post class with annotations)
  2. Additional Materials:
    • SB (Sutton and Barto) Chp 5.2, 5.4, 6.4-6.5, 6.7
Policy Gradient
  1. Lecture 5 Slides [Post lecture with annotations]
  2. Lecture 6 Slides [Post class annotations]
  3. Lecture 7 Slides [Post class annotations]
  4. Additional Materials:
    • SB (Sutton and Barto) Chp 13
Imitation Learning and Learning from Human Input and Batch RL
  1. Lecture 7 Slides [Post class annotations]
  2. Lecture 8 Slides (preclass) [Post class with annotations]
Data Efficient RL
  1. Lecture 9 Slides [Post class annotations]
  2. Lecture 10 Slides (no preclass) [Post class annotations]
  3. Lecture 11 Slides [Post class annotations]
  4. Lecture 12 Slides [Post class annotations]
  5. Additional Materials:
    6.
Ethics and Society Guest Lecture
  1. Lecture 10 Part Guest Slides (see latter half of slides)
Monte Carlo Tree Search and Conquering Go
  1. Lecture 13 Draft slides [Post class with annotations]
RL Guest Lecture
  1. Shane Gu: World of World Modeling