| Deep reinforcement learning for swarm systems M Hüttenrauch, A ©oąić, G Neumann Journal of Machine Learning Research 20 (54), 1-31, 2019 | 241 | 2019 |
| Guided deep reinforcement learning for swarm systems M Hüttenrauch, A ©oąić, G Neumann arXiv preprint arXiv:1709.06011, 2017 | 140 | 2017 |
| Guided deep reinforcement learning for swarm systems M Hüttenrauch, A ©oąić, G Neumann AAMAS Workshop on Autonomous Robots and Multirobot Systems, 2017 | 139 | 2017 |
| Guided Deep Reinforcement Learning for Swarm Systems GN Maximilian Hüttenrauch, Adrian ©oąić NIPS Workshop on Learning, Inference and Control of Multi-Agent Systems, 2016 | 139* | 2016 |
| Inverse Reinforcement Learning in Swarm Systems HK Adrian ©oąić, Wasiur R KhudaBukhsh, Abdelhak M Zoubir AAMAS Workshop on Transfer in Reinforcement Learning, 2017 | 71* | 2017 |
| Inverse reinforcement learning in swarm systems A ©oąić, WR KhudaBukhsh, AM Zoubir, H Koeppl arXiv preprint arXiv:1602.05450, 2016 | 71 | 2016 |
| Local communication protocols for learning complex swarm behaviors with deep reinforcement learning M Hüttenrauch, A ©oąić, G Neumann Swarm Intelligence: 11th International Conference, ANTS 2018, Rome, Italy …, 2018 | 36 | 2018 |
| Inverse reinforcement learning via nonparametric spatio-temporal subgoal modeling A ©oąić, E Rueckert, J Peters, AM Zoubir, H Koeppl Journal of Machine Learning Research 19 (69), 1-45, 2018 | 16 | 2018 |
| Correlation priors for reinforcement learning B Alt, A ©oąić, H Koeppl Advances in neural information processing systems 32, 2019 | 13 | 2019 |
| A Bayesian approach to policy recognition and state representation learning A ©oąić, AM Zoubir, H Koeppl IEEE Transactions on Pattern Analysis and Machine Intelligence 40 (6), 1295-1308, 2018 | 12 | 2018 |
| Reinforcement learning in a continuum of agents A ©oąić, AM Zoubir, H Koeppl Swarm Intelligence 12, 23-51, 2018 | 6 | 2018 |
| Inverse reinforcement learning via nonparametric subgoal modeling A ©oąic, AM Zoubir, H Koeppl AAAI spring symposium on data-efficient reinforcement learning, 2018 | 6 | 2018 |
| Learning complex swarm behaviors by exploiting local communication protocols with deep reinforcement learning M Hüttenrauch, A ©oąić, G Neumann arXiv preprint arXiv:1709.07224, 2017 | 6 | 2017 |
| Policy recognition via expectation maximization A Sosic, AM Zoubir, H Koeppl 2016 IEEE International Conference on Acoustics, Speech and Signal …, 2016 | 5 | 2016 |
| Learning models of behavior from demonstration and through interaction A ©oąić Technische Universität, 2018 | 2 | 2018 |
| sNN-LDS: spatio-temporal non-negative sparse coding for human action recognition T Guthier, A ©oąić, V Willert, J Eggert Artificial Neural Networks and Machine Learning–ICANN 2014: 24th …, 2014 | 2 | 2014 |
| Reinforcement learning in a continuum of agents (vol 12, pg 23, 2018) A Sosic, AM Zoubir, H Koeppl SWARM INTELLIGENCE 12 (4), 361-361, 2018 | | 2018 |
| Correction to: Reinforcement learning in a continuum of agents A ©oąić, AM Zoubir, H Koeppl Swarm Intelligence 12, 361-361, 2018 | | 2018 |
| Exploiting Local Communication Protocols for Learning Complex Swarm Behaviors with Deep Reinforcement Learning A Sosic, G Neumann, M Huttenrauch University of Lincoln, 2018 | | 2018 |
| Markov Assumptions for Non-negative Matrix Factorization A ©oąić | | 2013 |
Heinz KoepplTechnische Universität Darmstadt, Dept. Electrical Engineering and Dept. BiologyE-mail megerősítve itt: bcs.tu-darmstadt.de
