Reinforcement-Learning-course

Advanced course on Reinforcement Learning.

Sylabus

Module 1: Introduction to Reinforcement Learning

1. Overview of Reinforcement Learning and its applications
2. Markov Decision Processes (MDPs) and Bellman Equations
3. Q-Learning and SARSA algorithms

Module 2: Temporal-Difference methods

1. TD Learning
2. TD prediction
3. SARSA and TD control

Module 3: Monte Carlo Methods

1. First-Visit Monte Carlo and Every-Visit Monte Carlo methods
2. On-Policy and Off-Policy methods
3. Importance Sampling

Module 4: Function Approximation

1. Introduction to function approximation for Reinforcement Learning
2. Overview of Deep Reinforcement Learning
3. Hands-on experience with Gymnasium environment

Module 5: Project Work and Conclusion

1. Final project: students will work on a real-world Reinforcement Learning problem using the techniques and tools learned in the course
2. Course conclusion and future directions in Reinforcement Learning research

References:

1. "Reinforcement Learning: An Introduction" by Richard S. Sutton and Andrew G. Barto (2018)
2. Python implementation based on the book "Reinforcement Learning: An Introduction" by Richard S. Sutton and Andrew G. Barto (2018)
3. "Deep Reinforcement Learning Hands-On" by Maxim Lapan (2018)
4. Code for the book "Deep Reinforcement Learning Hands-On" by Maxim Lapan (2018)
5. Gymnasium environment (https://gymnasium.farama.org/)
6. TensorFlow documentation (https://www.tensorflow.org/guide)
7. PyTorch documentation (https://pytorch.org/docs/stable/index.html)
8. AlphaGo Documentary
9. Monte Carlo Tree Search Another Introduction