Deep Reinforcement Learning

Deep Reinforcement Learning for Autonomous Navigation in Intralogistics
Even with several advances in autonomous mobile robots, navigation in a highly dynamic environment still remains a challenge …
Harsh Yadav, Honghu Xue, Yan Rudall, Mohamed Bakr, Benedikt Hein, Elmar Rueckert, Ngoc Thinh Nguyen
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Deep Reinforcement Learning for Autonomous Navigation in Intralogistics
End-To-End Deep Reinforcement Learning for First-Person Pedestrian Visual Navigation in Urban Environments
We solve a pedestrian visual navigation problem with a first-person view in an urban setting via deep reinforcement learning in an end-to-end manner …
Honghu Xue, Rui Song, Julian Petzold, Benedikt Hein, Heiko Hamann, Elmar Rueckert
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End-To-End Deep Reinforcement Learning for First-Person Pedestrian Visual Navigation in Urban Environments
Using Deep Reinforcement Learning with Automatic Curriculum Learning for Mapless Navigation in Intralogistics
We propose a deep reinforcement learning approach for solving a mapless navigation problem in warehouse scenarios. In our approach, an automatic guided vehicle is equipped with two LiDAR sensors and one frontal RGB camera and learns to perform a targeted navigation task …
Honghu Xue, Benedikt Hein, Mohamed Bakr, Georg Schildbach, Bengt Abel, Elmar Rueckert
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Using Deep Reinforcement Learning with Automatic Curriculum Learning for Mapless Navigation in Intralogistics
ACNMP: Skill transfer and task extrapolation through learning from demonstration and reinforcement learning via representation sharing
To equip robots with dexterous skills, an effective approach is to first transfer the desired skill via Learning from Demonstration (LfD), then let the robot improve it …
M. Tuluhan Akbulut, Erhan Oztop, M. Yunus Seker, Honghu Xue, Ahmet E. Tekden, Emre Ugur
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ACNMP: Skill transfer and task extrapolation through learning from demonstration and reinforcement learning via representation sharing
Active transfer learning with neural networks through human-robot interactions (TRAIN)
The project receives funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – No 430054590. I offer support on providing Deep Reinforcement Learning algorithms.
Active transfer learning with neural networks through human-robot interactions (TRAIN)
Lecture "Reinforcement Learning"
I serve as the teaching assistant of the lecture “Reinforcement Learning - RO4100” in Univerisity of Luebeck.
Apr 27, 2020
Lecture "Reinforcement Learning"