M
Manuel Kroiss
Researcher at Ludwig Maximilian University of Munich
Publications - 12
Citations - 2874
Manuel Kroiss is an academic researcher from Ludwig Maximilian University of Munich. The author has contributed to research in topics: Reinforcement learning & Lineage (genetic). The author has an hindex of 6, co-authored 12 publications receiving 1356 citations. Previous affiliations of Manuel Kroiss include Technische Universität München.
Papers
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Journal ArticleDOI
Grandmaster level in StarCraft II using multi-agent reinforcement learning.
Oriol Vinyals,Igor Babuschkin,Wojciech Marian Czarnecki,Michael Mathieu,Andrew Dudzik,Junyoung Chung,David H. Choi,Richard E. Powell,Timo Ewalds,Petko Georgiev,Junhyuk Oh,Dan Horgan,Manuel Kroiss,Ivo Danihelka,Aja Huang,Laurent Sifre,Trevor Cai,John P. Agapiou,Max Jaderberg,Alexander Vezhnevets,Rémi Leblond,Tobias Pohlen,Valentin Dalibard,David Budden,Yury Sulsky,James Molloy,Tom Le Paine,Caglar Gulcehre,Ziyu Wang,Tobias Pfaff,Yuhuai Wu,Roman Ring,Dani Yogatama,Dario Wünsch,Katrina McKinney,Oliver Smith,Tom Schaul,Timothy P. Lillicrap,Koray Kavukcuoglu,Demis Hassabis,Chris Apps,David Silver +41 more
TL;DR: The agent, AlphaStar, is evaluated, which uses a multi-agent reinforcement learning algorithm and has reached Grandmaster level, ranking among the top 0.2% of human players for the real-time strategy game StarCraft II.
Journal ArticleDOI
Prospective identification of hematopoietic lineage choice by deep learning
Felix Buggenthin,Florian Buettner,Philipp S. Hoppe,Max Endele,Manuel Kroiss,Michael Strasser,Michael Schwarzfischer,Dirk Loeffler,Konstantinos D. Kokkaliaris,Oliver Hilsenbeck,Timm Schroeder,Fabian J. Theis,Carsten Marr +12 more
TL;DR: A deep neural network is presented that prospectively predicts lineage choice in differentiating primary hematopoietic progenitors using image patches from brightfield microscopy and cellular movement and allows identification of cells with differentially expressed lineage-specifying genes without molecular labeling.
Journal ArticleDOI
Quantifying alternative splicing from paired-end RNA-sequencing data
TL;DR: Novel data summaries and a Bayesian modeling framework are proposed that overcome limitations and determine biases in a non-parametric, highly flexible manner and allow to study alternative splicing patterns for individual samples and can also be the basis for downstream analyses.
Posted Content
What Can Learned Intrinsic Rewards Capture
Zeyu Zheng,Junhyuk Oh,Matteo Hessel,Zhongwen Xu,Manuel Kroiss,Hado van Hasselt,David Silver,Satinder Singh +7 more
TL;DR: This paper proposes a scalable meta-gradient framework for learning useful intrinsic reward functions across multiple lifetimes of experience and shows that it is feasible to learn and capture knowledge about long-term exploration and exploitation into a reward function.
Proceedings Article
What Can Learned Intrinsic Rewards Capture
Zeyu Zheng,Junhyuk Oh,Matteo Hessel,Zhongwen Xu,Manuel Kroiss,Hado van Hasselt,David Silver,Satinder Singh +7 more
TL;DR: In this paper, the authors propose a meta-gradient framework for learning useful intrinsic reward functions across multiple lifetimes of experience, which can generalise to other kinds of agents and to changes in the dynamics of the environment by capturing "what" the agent should strive to do.