Papers


Per group you pick a certain reinforcement learning research paper with associated codebase. This research paper forms the core of the course: you will try to understand it, replicate its experiments, test it in a new application and/or extend/improve the paper with a new idea. 


Note that the below papers are merrily suggestions: you are always free to come up with your own paper of interest. Do make sure that your paper comes with a trustworthy public codebase (ideally from the original authors of the paper). 



Model-free RL


Model-based RL (Survey)


Intrinsic Motivation (Survey)


Transformers in RL & RL from Human Feedback (Survey)


Multi-agent RL (Survey)