| 1 |
Introduction to Reinforcement Learning |
| 2 |
Markov Decision Processes (MDPs) |
| 3 |
Bellman Equations and Optimality |
| 4 |
Dynamic Programming: Value Iteration and Policy Iteration |
| 5 |
Monte Carlo Methods |
| 6 |
Temporal-Difference (TD) Learning |
| 7 |
Tabular Control: SARSA and Q-Learning |
| 8 |
Midterm |
| 9 |
Reinforcement Learning with Function Approximation |
| 10 |
Deep Q-Networks (DQN) and Stabilization Techniques |
| 11 |
Policy Gradient methods |
| 12 |
Actor–Critic methods |
| 13 |
Final Project Presentations (I) |
| 14 |
Final Project Presentations (II) |
