T
Tobias Pohlen
Researcher at RWTH Aachen University
Publications - 7
Citations - 3490
Tobias Pohlen is an academic researcher from RWTH Aachen University. The author has contributed to research in topics: Reinforcement learning & Segmentation. The author has an hindex of 6, co-authored 7 publications receiving 1881 citations.
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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.
Proceedings ArticleDOI
Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes
TL;DR: In this paper, a ResNet-like architecture is proposed to combine multi-scale context with pixel-level accuracy by using two processing streams within the network: one stream carries information at the full image resolution and the other stream undergoes a sequence of pooling operations to obtain robust features for recognition.
Posted Content
Observe and Look Further: Achieving Consistent Performance on Atari
Tobias Pohlen,Bilal Piot,Todd Hester,Mohammad Gheshlaghi Azar,Dan Horgan,David Budden,Gabriel Barth-Maron,Hado van Hasselt,John Quan,Mel Vecerik,Matteo Hessel,Rémi Munos,Olivier Pietquin +12 more
TL;DR: This paper proposes an algorithm that addresses three key challenges that any algorithm needs to master in order to perform well on all games: processing diverse reward distributions, reasoning over long time horizons, and exploring efficiently.
Posted Content
Full-Resolution Residual Networks for Semantic Segmentation in Street Scenes
TL;DR: This work proposes a novel ResNet-like architecture that exhibits strong localization and recognition performance, and combines multi-scale context with pixel-level accuracy by using two processing streams within the network.
Proceedings Article
Reward learning from human preferences and demonstrations in Atari
TL;DR: In this article, the authors combine two approaches: learning from expert demonstrations and learning from trajectory preferences to train a deep neural network to model the reward function and use its predicted reward to train an DQN-based deep RL agent on 9 Atari games.