# Schedule
This is a directed study course in Computer Science. In Fall 2026, the group will investigate optimal information design, for example, Bayesian persuasion, in particular, its applications in data poisoning and adversarial attacks on reinforcement learners.
| Week |
Date |
Topic |
Notes |
| 1 |
- |
Markov Games |
W1 |
| 2 |
- |
Markov Perfect Equilibrium |
W2 |
| 3 |
- |
Partial Observability |
W3 |
| 4 |
- |
Belief State Markov Game |
W4 |
| 5 |
- |
Signaling Game |
W5 |
| 6 |
- |
Mechanism Design |
W6 |
| 7 |
- |
Bayesian Persuasion |
W7 |
| 8 |
- |
Information Design |
W8 |
| 9 |
- |
Project |
W9 |
| 10 |
- |
Project |
W10 |
| 11 |
- |
Project |
W11 |
| 12 |
- |
Project |
W12 |
| 13 |
- |
Project |
W13 |
| 14 |
- |
Project |
W14 |
| 15 |
- |
Project |
W15 |
Textbook (main):
📗 Algorithmic Game Theory:
Link.
Notes from previous semesters:
📗 Reinforcement Learning for Robotics:
Link 📗 Continuous Action Games:
Link 📗 Partial Observability:
Link 📗 Multi-agent Deep Learning:
Link 📗 Single-agent Deep Learning:
Link 📗 Multi-agent Reinforcement Learning:
Link