Zobrazeno 1 - 10
of 5 656
pro vyhledávání: '"Formanek"'
Autor:
Daniel, Jemma, de Kock, Ruan, Nessir, Louay Ben, Abramowitz, Sasha, Mahjoub, Omayma, Khlifi, Wiem, Formanek, Claude, Pretorius, Arnu
The Transformer model has demonstrated success across a wide range of domains, including in Multi-Agent Reinforcement Learning (MARL) where the Multi-Agent Transformer (MAT) has emerged as a leading algorithm in the field. However, a significant draw
Externí odkaz:
http://arxiv.org/abs/2410.19382
Autor:
Ramsey, D., Malaca, B., Simpson, T. T., Formanek, M., Mack, L. S., Vieira, J., Froula, D. H., Palastro, J. P.
Laser-driven free-electron lasers (LDFELs) replace magnetostatic undulators with the electromagnetic fields of a laser pulse. Because the undulator period is half the wavelength of the laser pulse, LDFELs can amplify x rays using lower electron energ
Externí odkaz:
http://arxiv.org/abs/2410.12975
Autor:
Mahjoub, Omayma, Abramowitz, Sasha, de Kock, Ruan, Khlifi, Wiem, Toit, Simon du, Daniel, Jemma, Nessir, Louay Ben, Beyers, Louise, Formanek, Claude, Clark, Liam, Pretorius, Arnu
As the field of multi-agent reinforcement learning (MARL) progresses towards larger and more complex environments, achieving strong performance while maintaining memory efficiency and scalability to many agents becomes increasingly important. Althoug
Externí odkaz:
http://arxiv.org/abs/2410.01706
An exact solution of the Dirac equation in the presence of an arbitrary electromagnetic plane wave is found, which corresponds to a focused electron wave packet, with the focus of the wave packet moving at the speed of light in the opposite direction
Externí odkaz:
http://arxiv.org/abs/2409.15025
Offline multi-agent reinforcement learning (MARL) is an exciting direction of research that uses static datasets to find optimal control policies for multi-agent systems. Though the field is by definition data-driven, efforts have thus far neglected
Externí odkaz:
http://arxiv.org/abs/2409.12001
Offline multi-agent reinforcement learning (MARL) leverages static datasets of experience to learn optimal multi-agent control. However, learning from static data presents several unique challenges to overcome. In this paper, we focus on coordination
Externí odkaz:
http://arxiv.org/abs/2407.01343
Autor:
Sotolar, Ondrej, Formanek, Vojtech, Debnath, Alok, Lahnala, Allison, Welch, Charles, FLek, Lucie
Empathetic response generation is a desirable aspect of conversational agents, crucial for facilitating engaging and emotionally intelligent multi-turn conversations between humans and machines. Leveraging large language models for this task has show
Externí odkaz:
http://arxiv.org/abs/2406.19071
Self-consistent strong plasma screening around light nuclei is implemented in the Big Bang nucleosynthesis (BBN) epoch to determine the short-range screening potential, $e\phi(r)/T \geq 1$, relevant for thermonuclear reactions. We numerically solve t
Externí odkaz:
http://arxiv.org/abs/2406.13055
Offline multi-agent reinforcement learning (MARL) is an emerging field with great promise for real-world applications. Unfortunately, the current state of research in offline MARL is plagued by inconsistencies in baselines and evaluation protocols, w
Externí odkaz:
http://arxiv.org/abs/2406.09068
We characterize in a novel manner the physical properties of the low temperature Fermi gas in the degenerate domain as a function of temperature and chemical potential. For the first time we obtain low temperature $T$ results in the domain where seve
Externí odkaz:
http://arxiv.org/abs/2405.05287