Johannes Ackermann
I’m a third-year PhD student at the University of Tokyo, working on Reinforcement Learning supervised by Professor Masashi Sugiyama.
I’m particularly interested in how we can deal with changing or complicated transition and reward functions in RL, as encountered in Multi-Agent interaction, Multi-Task settings or due to Non-Stationarity.
I’m also employed by RIKEN AIP as a part-time researcher and supported by a Japanese government MEXT scholarship, and a Microsoft Research Asia D-CORE grant.
I interned at Preferred Networks and previously worked as a DSP researcher in Huawei’s Munich Research Center, working on applied Machine Learning. Before that, I received my B.Sc. and M.Sc. in Electrical Engineering and Information Technology from the Technical University of Munich in 2018 and 2021. I wrote my Master’s Thesis about Multi-Task RL in ETH Zurich’s Distributed Computing group.
news
May 17, 2024 | Our work on Offline Reinforcement Learning from Datasets with Structured Non-Stationarity was accepted at RLC 2024 |
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Dec 05, 2022 | I presented our work High-Resolution Image Editing via Multi-Stage Blended Diffusion from my internship in PFN at the NeurIPS Machine Learning for Creativity and Design Workshop |
Apr 20, 2022 | I posted a blog-entry detailing how I built a Text to Image web app! |
latest posts
Apr 20, 2022 | Building a Text to Image Web App |
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publications
- Leveraging Domain-Unlabeled Data in Offline Reinforcement Learning across Two DomainsAug 2024Preprint
- Reducing Overestimation Bias in Multi-Agent Domains Using Double Centralized CriticsIn Deep Reinforcement Learning Workshop at NeurIPS , Dec 2019