Schedule
The schedule is tentative and subject to change. We will have guest lectures and may accomodate the schedule accordingly.
Date | Topic | Files and Reading |
---|---|---|
Mar 26 | Course overview | Slides, Book Ch. 1 |
Mar 28 | Intro to Markov Decision Processes | Slides, Book Ch. 2,3 |
Apr 2 | Planning with a known model in the tabular case | Slides, Book Ch. 4 |
Apr 4 | Policy Iteration (contd) and MuJoCo setup | Slides, Book Ch. 4 |
Apr 9 | Policy gradient methods - I | Slides, policy gradient |
Apr 11 | Policy gradient methods - II | Slides, policy gradient |
Apr 16 | Off-policy learning - I | |
Apr 18 | Off-policy learning - II | |
Apr 23 | Imitation learning | |
Apr 25 | MCTS and UC Trees | Slides |
Apr 30 | Trajectory Optimization | Slides |
May 2 | Combining Trajectories and Policies | Slides |
May 7 | Guest lecture - Prof. Emanuel Todorov (UW) | Slides |
May 9 | Guest lecture - Dr. Igor Mordatch (OpenAI) | |
May 14 | Guest lecture - Dr. Vikash Kumar (Google Brain) | Slides |
May 16 | No class | |
May 21 | Learning to learn, meta learning | |
May 23 | General duality between control & inference, compositionality in LDMPs | |
May 28 | No class | |
May 30 | Hierarchical RL | Slides |