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Group by: No Grouping | Item Type | Date | Language
Number of items: 14.

Article

Hüttenrauch, M. and Šošić, A. and Neumann, G. :
Deep Reinforcement Learning for Swarm Systems.
[Online-Edition: http://jmlr.csail.mit.edu/papers/volume20/18-476/18-476.pdf]
In: Journal of Machine Learning Research, 20 (54) pp. 1-31.
[Article] , (2019)

Šošić, A. and Zoubir, A. M. and Koeppl, H. :
A Bayesian Approach to Policy Recognition and State Representation Learning.
[Online-Edition: https://doi.org/10.1109/TPAMI.2017.2711024]
In: IEEE Transactions on Pattern Analysis and Machine Intelligence, 40 (6) pp. 1295-1308.
[Article] , (2018)

Šošić, A. and Zoubir, A. M. and Koeppl, H. :
Reinforcement Learning in a Continuum of Agents.
[Online-Edition: http://rdcu.be/wKay]
In: Swarm Intelligence, 12 (1) pp. 23-51.
[Article] , (2018)

Šošić, A. and Rueckert, E. and Peters, J. and Zoubir, A. M. and Koeppl, H. :
Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal Modeling.
[Online-Edition: http://www.jmlr.org/papers/volume19/18-113/18-113.pdf]
In: Journal of Machine Learning Research, 19 (69) pp. 1-45.
[Article] , (2018)

Book Section

Guthier, T. and Šošić, A. and Willert, V. and Eggert, J. :
sNN-LDS: Spatio-temporal Non-negative Sparse Coding for Human Action Recognition.
[Online-Edition: http://dx.doi.org/10.1007/978-3-319-11179-7_24]
In: Artificial Neural Networks and Machine Learning – ICANN 2014. Lecture Notes in Computer Science, 8681. Springer International Publishing , pp. 185-192.
[Book Section] , (2014)

Conference or Workshop Item

Šošić, A. and Zoubir, A. M. and Koeppl, H. :
Inverse Reinforcement Learning via Nonparametric Subgoal Modeling.
[Online-Edition: https://aaai.org/ocs/index.php/SSS/SSS18/paper/view/17531/15...]
In: AAAI Spring Symposium on Data-Efficient Reinforcement Learning .
[Conference or Workshop Item] , (2018)

Hüttenrauch, M. and Šošić, A. and Neumann, G. :
Local Communication Protocols for Learning Complex Swarm Behaviors With Deep Reinforcement Learning.
In: International Conference on Swarm Intelligence .
[Conference or Workshop Item] , (2018)

Hüttenrauch, M. and Šošić, A. and Neumann, G. :
Guided Deep Reinforcement Learning for Swarm Systems.
[Online-Edition: https://arxiv.org/abs/1709.06011]
In: AAMAS Workshop on Autonomous Robots and Multirobot Systems.
[Conference or Workshop Item] , (2017)

Šošić, A. and KhudaBukhsh, W. R. and Zoubir, A. M. and Koeppl, H. :
Inverse Reinforcement Learning in Swarm Systems.
In: AAMAS Workshop on Transfer in Reinforcement Learning .
[Conference or Workshop Item] , (2017)

Šošić, A. and KhudaBukhsh, W. R. and Zoubir, A. M. and Koeppl, H. :
Inverse Reinforcement Learning in Swarm Systems (Best Paper Award Finalist).
[Online-Edition: http://dl.acm.org/citation.cfm?id=3091320]
In: International Conference on Autonomous Agents and Multiagent Systems .
[Conference or Workshop Item] , (2017)

Hüttenrauch, M. and Šošić, A. and Neumann, G. :
Guided Deep Reinforcement Learning for Swarm Systems.
In: NIPS Workshop on Learning, Inference and Control of Multi-Agent Systems.
[Conference or Workshop Item] , (2016)

Šošić, A. and Zoubir, A. M. and Koeppl, H. :
Policy Recognition via Expectation Maximization.
[Online-Edition: https://doi.org/10.1109/icassp.2016.7472589]
In: IEEE International Conference on Acoustics, Speech and Signal Processing.
[Conference or Workshop Item] , (2016)

Guthier, T. and Šošić, A. and Willert, V. and Eggert, J. :
Finding a Tradeoff between Compression and Loss in Motion Compensated Video Coding.
In: SIGMAP and WINSYS 2012 - Proceedings of the International Conference on Signal Processing and Multimedia Applications and International Conference on Wireless Information Networks and Systems, Rome, Italy, 24-27 July, 2012, SIGMAP is part of ICETE - The I.
[Conference or Workshop Item] , (2012)

Ph.D. Thesis

Šošić, A. :
Learning Models of Behavior From Demonstration and Through Interaction.
[Online-Edition: https://tuprints.ulb.tu-darmstadt.de/8107]
Technische Universität , Darmstadt
[Ph.D. Thesis], (2018)

This list was generated on Tue May 21 00:46:41 2019 CEST.