Lecture Materials

Lecture Materials

Lecture materials for this course are given below. Note the associated refresh your understanding and check your understanding polls will be posted weekly.
Topic   Videos (on Canvas/Panopto)  Course Materials  
Introduction to Reinforcement Learning
  • Lecture 1
    		•
    1. Lecture 1 Draft Slides [Post class version]
    2. Additional Materials:
    Tabular MDP planning
  • Lecture 2
    		•
    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
  • Lecture 3
    		•
    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
      • David Silver's Lecture 4 [link]
    Q-learning
  • Lecture 4
    		•
    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
  • Lecture 5
  • Lecture 6
  • Lecture 7
    		•
    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
  • Lecture 8
  • Lecture 9 (including DPO guest lecture by Rafael Rafailov, Archit Sharma, Eric Mitchell
    		•
    1. Lecture 7 Slides [Post class annotations]
    2. Lecture 8 Slides (preclass) [Post class with annotations]
    3. Lecture 9 Slides [Post class]
    4. Lecture 10 Slides [Post class]
    5. Additional Materials: