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Maximilian Hüttenrauch
Maximilian Hüttenrauch
kit.edu의 이메일 확인됨
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Deep reinforcement learning for swarm systems
M Hüttenrauch, A Šošić, G Neumann
Journal of Machine Learning Research 20 (54), 1-31, 2019
2822019
Guided deep reinforcement learning for swarm systems
M Hüttenrauch, A Šošić, G Neumann
arXiv preprint arXiv:1709.06011, 2017
1682017
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
422018
Deep reinforcement learning for attacking wireless sensor networks
J Parras, M Hüttenrauch, S Zazo, G Neumann
Sensors 21 (12), 4060, 2021
102021
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
72017
Using m-embeddings to learn control strategies for robot swarms
GHW Gebhardt, M Hüttenrauch, G Neumann
Swarm Intelligence, 2019
62019
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
12024
Regret-aware black-box optimization with natural gradients, trust-regions and entropy control
M Hüttenrauch, G Neumann
arXiv preprint arXiv:2206.06090, 2022
12022
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
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