Zobrazeno 1 - 10
of 299
pro vyhledávání: '"Karl, Holger"'
Autor:
Paeleke, Leonard, Keshtiarast, Navid, Seehofer, Paul, Bless, Roland, Karl, Holger, Petrova, Marina, Zitterbart, Martina
6G networks will be highly dynamic, re-configurable, and resilient. To enable and support such features, employing AI has been suggested. Integrating AIin networks will likely require distributed AI deployments with resilient connectivity, e.g., for
Externí odkaz:
http://arxiv.org/abs/2410.11565
Finding efficient routes for data packets is an essential task in computer networking. The optimal routes depend greatly on the current network topology, state and traffic demand, and they can change within milliseconds. Reinforcement Learning can he
Externí odkaz:
http://arxiv.org/abs/2410.10377
The Intelligent Transportation System (ITS) environment is known to be dynamic and distributed, where participants (vehicle users, operators, etc.) have multiple, changing and possibly conflicting objectives. Although Reinforcement Learning (RL) algo
Externí odkaz:
http://arxiv.org/abs/2403.08879
Autor:
Kirchner, Valentin, Karl, Holger
Operators of reconfigurable wavelength-division multiplexed (WDM) optical networks adapt the lightpath topology to balance load and reduce transmission delays. Such an adaption generally depends on a known or estimated traffic matrix. Network functio
Externí odkaz:
http://arxiv.org/abs/2306.03041
We generalize the Borkar-Meyn stability Theorem (BMT) to distributed stochastic approximations (SAs) with information delays that possess an arbitrary moment bound. To model the delays, we introduce Age of Information Processes (AoIPs): stochastic pr
Externí odkaz:
http://arxiv.org/abs/2305.07091
We formulate offloading of computational tasks from a dynamic group of mobile agents (e.g., cars) as decentralized decision making among autonomous agents. We design an interaction mechanism that incentivizes such agents to align private and system g
Externí odkaz:
http://arxiv.org/abs/2208.04237
We propose a multi-agent distributed reinforcement learning algorithm that balances between potentially conflicting short-term reward and sparse, delayed long-term reward, and learns with partial information in a dynamic environment. We compare diffe
Externí odkaz:
http://arxiv.org/abs/2204.02268
We formulate computation offloading as a decentralized decision-making problem with autonomous agents. We design an interaction mechanism that incentivizes agents to align private and system goals by balancing between competition and cooperation. The
Externí odkaz:
http://arxiv.org/abs/2204.02267
Iterative distributed optimization algorithms involve multiple agents that communicate with each other, over time, in order to minimize/maximize a global objective. In the presence of unreliable communication networks, the Age-of-Information (AoI), w
Externí odkaz:
http://arxiv.org/abs/2201.11343