Reinforcement Learning Lecture - WS 2024/25

This lecture is part of the Machine Learning Masters program at the University of Tübingen. The course is run by the Autonomous Learning Group.

• Tue 14:15 - 15:45: lecture in N10 (Bio Hörsaalgebäude AdM 3) on Morgenstelle 3  (starting on October 15th)
• Tue. 16:00 - 18:00: tutorial in D8H08 (D-Bau [Physik]) , D7H41 (D-Bau [Physik]), and Hörsaal TTR2 (Cyber Valley - TTR2)  (starting on October 22th)

The course will provide you with theoretical and practical knowledge of reinforcement learning, a field of machine learning concerned with decision-making and interaction with dynamical systems, such as robots. We start with a brief overview of supervised learning and spend the most time on reinforcement learning. The exercises will help you get hands-on with methods and deepen your understanding.

Students gain an understanding of reinforcement learning formulations, problems, and algorithms on a theoretical and practical level. After this course, students should be able to implement and apply deep reinforcement learning algorithms to new problems. 

Both slides and exercises are available on ILIAS.

1. Lecture 1 Introduction to the course, Reinforcement Learning (RL) History and RL setup; Background reading: Sutton and Barto Reinforcement learning for the next few lectures (for this lecture, parts of Chapter 3)
2. Lecture 2 MDPs; Background reading: Sutton and Barto Reinforcement learning Chapter 4
3. Lecture 3 Model-free Prediction; Background reading: Sutton and Barto Reinforcement learning First part of Chapters 5, 6, 7, 12
4. Lecture 4 Model-free Control; Background reading: Sutton and Barto Reinforcement learning Chapters 5.2, 5.3, 5.5, 6.4, 6.5, 12.7
5. Lecture 5 Neural Networks and Imitation Learning; Background reading: C.M. Bishop Pattern Recognition and Machine Learning, Chap. 5
6. Lecture 6: Value Function Approximation; Background reading: Sutton and Barto Reinforcement learning Chapters 9.1-9.8, 10.1, 10.2, 11.1-11.3. Supplementary: DQN paper 1, paper 2, NFQ paper
7. Lecture 7: Policy Gradient; Background reading: Sutton and Barto Reinforcement learning Chapters 13
8. Lecture 8: Policy Gradient and Actor-Critic; Background reading: Natural Actor Critic Paper, TRPO Paper, PPO Paper
9. Lecture 9: Q-learning style Actor-Critic; Background reading: DPG Paper, DDPG Paper, TD3 Paper, SAC paper
10. Lecture 10: Exploration and Tricks to improve Deep RL (with recent work from my group); Background reading:  ICM Paper, RND Paper, Pink-Noise Paper, HER paper 
11. Lecture 11: Model-based Methods: Dyna-Q, MBPO; Background reading: Sutton and Barto Reinforcement learning Chapters 8 and the MBPO Paper
12. Lecture 12: Model-based Methods II: Online-Planning: (with recent work from my group); CEM, PETS, iCEM; CEE-US; Background reading: PETS paper, iCEM paper (video), CEE-US paper (videos)
13. Lecture 13: Alpha Go and Alpha Zero, Dreamer. Background reading: AlphaGo paper (also in Ilias, because behind the paywall), AlphaZero Paper, and Dreamer Paper
14. Lecture 14: Offline RL. Background reading: CQL paper, CRR paper, Benchmarking paper

• Sutton & Barto, Reinforcement Learning: An Introduction
• Bertsekas, Dynamic Programming and Optimal Control, Vol. 1
• Bishop, Pattern Recognition and Machine Learning

• Andrew Ng's ML course on Coursera
• David Silver's RL lectures
• Sergey Levine's deep RL course
• Dimitri Bertsekas's lectures on dynamic programming and stochastic control