Deep reinforcement learning for swarm systems M Hüttenrauch, A Šošić, G Neumann Journal of Machine Learning Research 20 (54), 1-31, 2019 | 278 | 2019 |
Guided deep reinforcement learning for swarm systems M Hüttenrauch, A Šošić, G Neumann arXiv preprint arXiv:1709.06011, 2017 | 163 | 2017 |
Local communication protocols for learning complex swarm behaviors with deep reinforcement learning M Hüttenrauch, A Šošić, G Neumann Swarm Intelligence: 11th International Conference, ANTS 2018, Rome, Italy …, 2018 | 41 | 2018 |
Deep reinforcement learning for attacking wireless sensor networks J Parras, M Hüttenrauch, S Zazo, G Neumann Sensors 21 (12), 4060, 2021 | 10 | 2021 |
Learning complex swarm behaviors by exploiting local communication protocols with deep reinforcement learning M Hüttenrauch, A Šošić, G Neumann arXiv preprint arXiv:1709.07224, 2017 | 7 | 2017 |
Using m-embeddings to learn control strategies for robot swarms GHW Gebhardt, M Hüttenrauch, G Neumann Swarm Intelligence, 2019 | 6 | 2019 |
Robust Black-Box Optimization for Stochastic Search and Episodic Reinforcement Learning M Hüttenrauch, G Neumann Journal of Machine Learning Research 25 (153), 1-44, 2024 | 1 | 2024 |
Regret-aware black-box optimization with natural gradients, trust-regions and entropy control M Hüttenrauch, G Neumann arXiv preprint arXiv:2206.06090, 2022 | 1 | 2022 |
Information-Theoretic Trust Regions for Stochastic Gradient-Based Optimization P Dahlinger, P Becker, M Hüttenrauch, G Neumann arXiv preprint arXiv:2310.20574, 2023 | | 2023 |
Coordinate ascent MORE with adaptive entropy control for population-based regret minimization M Hüttenrauch, G Neumann Proceedings of the Genetic and Evolutionary Computation Conference Companion …, 2021 | | 2021 |
On Representations and Exploration in Policy Search Methods for Multi-Agent Robotics M Hüttenrauch Dissertation, Karlsruhe, Karlsruher Institut für Technologie (KIT), 2024, 0 | | |
Information Theoretic Trust Regions for Gradient Descent G Neumann, M Hüttenrauch, P Becker | | |