Curriculum

1. Foundations of Reinforcement Learning

   4 lessons

   1. 00 Welcome to RL - Curriculum and Prerequisites
   2. 01 Introduction to Reinforcement Learning
   3. 02 Markov Decision Process Preliminaries
   4. 03 Markov Decision Process

2. Foundations of Reinforcement Learning II

   5 lessons

   1. 04 Training Framework and Hands-on
   2. 05 Tabular MDP - Policy Iteration - Dynamic Programming
   3. 06 Tabular MDP and Policy Iteration - MC and TD
   4. 07 Generalized Policy Iteration
   5. 08 Q-Learning

3. Model-Free Online Deep Reinforcement Learning

   8 lessons

   1. 09 Deep Q-Learning
   2. 10 Deep Q-Learning II
   3. 11 Policy Gradient 1
   4. 12 Policy Gradient 2
   5. 13 Policy Gradient 3 - On-policy Actor Critic
   6. 14 Policy Gradient 4 - PPO
   7. 15 Off-policy Actor-Critic
   8. 16 Soft Actor-Critic

4. Applications and Advanced Topics

   5 lessons

   1. 17 RL for LLMs
   2. 18 RL for LLMs 2
   3. 19 Offline RL - Conservative Q-Learning
   4. 20 Offline RL 2
   5. 21 Imitation Learning

5. Course Materials

   1 lesson

   1. Slides and Codes

6. Course Review

   1 lesson

   1. Course Review Form