Course Overview

  1. Introduction to Deep Reinforcement Learning (DRL)
  2. MDP : Markov Decision Process
  3. Dynamic Programming
  4. Monte Carlo Methods
  5. Temporal Difference Learning
  6. Policy Gradient Methods
  7. Actor-Critic Methods
  8. Deep Q-Networks (DQN)
  9. Proximal Policy Optimization (PPO)
  10. Deep Deterministic Policy Gradient (DDPG)
  11. Soft Actor-Critic (SAC)
  12. Multi-Agent Reinforcement Learning (MARL)
  13. Applications of DRL in various domains (e.g., robotics, gaming, finance)
  14. Challenges and Future Directions in DRL
  15. Hands-on Projects and Case Studies
  16. Conclusion and Summary of Key Concepts
  17. Q&A and Discussion