The Machine Learning Reading Group (MLRG) of the AICommunityOWL has the goal to get a better understanding of current trends in machine learning on a technical level. The target audience are researchers and practitioners in the field of machine learning. We read and discuss current papers with a high media impact or prominent positioning (at least orals) of the leading conferences, e.g. NeurIPS, ICML, ICLR, AISTATS, UAI, COLT, KDD, AAAI, CVPR, ACL, or IJCAI. Attendees are expected to have read (or skimmed) the papers that are going to be presented so as not to be thrown off by the notation or problem statement and to be able to participate in informed discussions related to the paper. Suggestions for future papers are encouraged, as are volunteer presenters.
We hold our first online meeting (after a yearlong hiatus) on Tuesday, May 4th, at 16:00 CET under this link.
Don’t miss the date and save the event to your calendar:
Next Session:
Recent Breakthroughs in Mastering Complex Video Games with Deep Reinforcement Learning
Abstract:
StarCraft was long considered an unsolvable game, using AI methods. This has been proven wrong by DeepMind in 2019 when their reinforcement learning agent achieved Grandmaster level in StarCraft II. We want to discuss some of the technical aspects of AlphaStar and also take a brief look at other challenging games tackled using reinforcement learning methods.
Links:
https://www.seas.upenn.edu/~cis520/papers/RL_for_starcraft.pdf (AlphaStar)
https://arxiv.org/pdf/1912.06680.pdf (OpenAI Dota)
https://arxiv.org/pdf/1901.10995.pdf (GoExplore)
Speakers:
Arthur Müller (Fraunhofer IOSB-INA) and Andreas Besginow (TH OWL)
For questions or suggestions of topics, feel free to contact markus.lange-hegermann@th-owl.de
