Zobrazeno 1 - 10
of 518
pro vyhledávání: '"Thomy, P."'
Anytime multi-agent path finding (MAPF) is a promising approach to scalable path optimization in multi-agent systems. MAPF-LNS, based on Large Neighborhood Search (LNS), is the current state-of-the-art approach where a fast initial solution is iterat
Externí odkaz:
http://arxiv.org/abs/2408.02960
Autor:
Kölle, Michael, Schneider, Karola, Egger, Sabrina, Topp, Felix, Phan, Thomy, Altmann, Philipp, Nüßlein, Jonas, Linnhoff-Popien, Claudia
In recent years, Multi-Agent Reinforcement Learning (MARL) has found application in numerous areas of science and industry, such as autonomous driving, telecommunications, and global health. Nevertheless, MARL suffers from, for instance, an exponenti
Externí odkaz:
http://arxiv.org/abs/2407.20739
Autor:
Altmann, Philipp, Winter, Katharina, Kölle, Michael, Zorn, Maximilian, Phan, Thomy, Linnhoff-Popien, Claudia
Recent advances in multi-agent systems (MAS) have shown that incorporating peer incentivization (PI) mechanisms vastly improves cooperation. Especially in social dilemmas, communication between the agents helps to overcome sub-optimal Nash equilibria
Externí odkaz:
http://arxiv.org/abs/2404.03431
Autor:
Chan, Shao-Hung, Chen, Zhe, Lin, Dian-Lun, Zhang, Yue, Harabor, Daniel, Huang, Tsung-Wei, Koenig, Sven, Phan, Thomy
Multi-Agent Path Finding (MAPF) is the problem of finding a set of collision-free paths for multiple agents in a shared environment while minimizing the sum of travel time. Since solving the MAPF problem optimally is NP-hard, anytime algorithms based
Externí odkaz:
http://arxiv.org/abs/2402.01961
A wide range of real-world applications can be formulated as Multi-Agent Path Finding (MAPF) problem, where the goal is to find collision-free paths for multiple agents with individual start and goal locations. State-of-the-art MAPF solvers are mainl
Externí odkaz:
http://arxiv.org/abs/2401.05860
Autor:
Müller, Robert, Turalic, Hasan, Phan, Thomy, Kölle, Michael, Nüßlein, Jonas, Linnhoff-Popien, Claudia
In the realm of Multi-Agent Reinforcement Learning (MARL), prevailing approaches exhibit shortcomings in aligning with human learning, robustness, and scalability. Addressing this, we introduce ClusterComm, a fully decentralized MARL framework where
Externí odkaz:
http://arxiv.org/abs/2401.03504
Anytime multi-agent path finding (MAPF) is a promising approach to scalable path optimization in large-scale multi-agent systems. State-of-the-art anytime MAPF is based on Large Neighborhood Search (LNS), where a fast initial solution is iteratively
Externí odkaz:
http://arxiv.org/abs/2312.16767
Autor:
Altmann, Philipp, Stein, Jonas, Kölle, Michael, Bärligea, Adelina, Gabor, Thomas, Phan, Thomy, Feld, Sebastian, Linnhoff-Popien, Claudia
Quantum computing (QC) in the current NISQ era is still limited in size and precision. Hybrid applications mitigating those shortcomings are prevalent to gain early insight and advantages. Hybrid quantum machine learning (QML) comprises both the appl
Externí odkaz:
http://arxiv.org/abs/2312.11337
Autor:
Kölle, Michael, Topp, Felix, Phan, Thomy, Altmann, Philipp, Nüßlein, Jonas, Linnhoff-Popien, Claudia
Multi-Agent Reinforcement Learning is becoming increasingly more important in times of autonomous driving and other smart industrial applications. Simultaneously a promising new approach to Reinforcement Learning arises using the inherent properties
Externí odkaz:
http://arxiv.org/abs/2311.05546
Autor:
R. Loritz, A. Dolich, E. Acuña Espinoza, P. Ebeling, B. Guse, J. Götte, S. K. Hassler, C. Hauffe, I. Heidbüchel, J. Kiesel, M. Mälicke, H. Müller-Thomy, M. Stölzle, L. Tarasova
Publikováno v:
Earth System Science Data, Vol 16, Pp 5625-5642 (2024)
Comprehensive large-sample hydrological datasets, particularly the CAMELS datasets (Catchment Attributes and MEteorology for Large-sample Studies), have advanced hydrological research and education in recent years. These datasets integrate extensive
Externí odkaz:
https://doaj.org/article/022ee25c3f5a4853ac2b60ec5eeed54c
