Seminar in Deep Reinforcement Learning (FS 2020)
As a seminar participant, you are invited to attend all the talks and make a presentation. Your presentation should be in English. The presentation should last 35 minutes plus about 10 minutes of discussion.
Disclaimer: This is a seminar, we will focus on reasearch and skip most of the basics. If you feel like you cannot follow the discussions we invite you to check out this lecture or at least this talk.
Presentation & Discussion
The seminar will be exciting if the presentations are exciting. Here is a 2 page guideline how to do a great scientific presentation. Here are some additional guidelines: 1, 2 and 3. You can find further guidance to structure your talk as well as resources and ideas about what each topic should address here.
We further expect the presentation to motivate a lively discussion. We encourage discussions during and after the presentations as a main objective of this seminar. It may help discussions if you also try to be critical about the presented work. These are all scientific papers, but if you have been in science long enough...
Your grade will mostly depend on your presentation. In addition, we also grade how actively you participate in the discussions throughout the whole semester. Further, there will be a programming challenge alongside the seminar, in which you can take part to improve your grade.
Will be introduced in the first seminar.
How To Sign Up
There will be two presentation per week, so there is a limited number of slots (topics) which will be assigned based on preference. If you have not received a mail so far, confirming your spot in the seminar, write a sentence regarding your background (courses, projects, ...) in deep reinforcement learning to Oliver Richter, to get a spot on the waiting list, in case someone cancels.
After You Got Your TopicWe established the following rules to ensure a high quality of the talks and hope that these will result in a good grade for you:
- At least 5 weeks before your talk: first meeting with your mentor (you need to read the assigned literature before this meeting).
- At least 3 weeks before your talk: meet your mentor to discuss the structure of your talk.
- At least 1 week before your talk: give the talk in front of your mentor who will provide feedback.
- At the presentation date we expect an electronic copy of your slides.
|25.02.2020||Zhao Ma, Constantin Le Clei||Deep Learning and Neural Architecture||Zhao Meng|
|03.03.2020||Alexander Nedergaard, Xiang Li||On Policy vs. Off-Policy vs. Batch-Policy Learning||Gino Brunner|
|10.03.2020||Samriddhi Jain, Yunke Ao||Deep reinforcement learning in continuous action spaces||Oliver Richter|
|17.03.2020||Lucas Brunner, Cliff Li||Hierarchical deep reinforcement learning||Béni Egressy|
|24.03.2020||Jérémy Scheurer, Tommaso Macri||Deep reinforcement learning and stochastic planning in games||Kevin Roth|
|31.03.2020||Lee Sharkey, Ajaykumar Unagar||Model based vs. model free deep reinforcement learning||Pascal Weber|
|07.04.2020||Thomas Langerak, Sébastien Foucher||Deep reinforcement learning in partial observability||Lukas Faber|
|21.04.2020||Orhan Saeedi, Florian Turati||Multi-Armed Bandits||Ye Wang|
|28.04.2020||Le Chen, Felix Schur||Non-differentiable optimization||Henri Devillez|
|05.05.2019||Steven Battilana, Philippe Blatter||Meta-Learning||Giambattista Parascandolo|
|12.05.2020||-||No seminar - free time for coding challenge|
|19.05.2020||Ramon Witschi, Nicolas Zucchet||Continual Learning||Damian Pascual|
|26.05.2020||Oliver Richter||Discussion and Review|