Zobrazeno 1 - 10
of 5 761
pro vyhledávání: '"P Pretorius"'
Motivated by understanding the nonlinear gravitational dynamics of spacetimes admitting stably trapped null geodesics, such as ultracompact objects and black string solutions to general relativity, we explore the dynamics of nonlinear scalar waves on
Externí odkaz:
http://arxiv.org/abs/2411.17445
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:
Afshordi, Niayesh, Ashtekar, Abhay, Barausse, Enrico, Berti, Emanuele, Brito, Richard, Buoninfante, Luca, Carballo-Rubio, Raúl, Cardoso, Vitor, Carullo, Gregorio, Dafermos, Mihalis, De Laurentis, Mariafelicia, del Rio, Adrian, Di Filippo, Francesco, Eichhorn, Astrid, Emparan, Roberto, Gregory, Ruth, Herdeiro, Carlos A. R., Kunz, Jutta, Lehner, Luis, Liberati, Stefano, Mathur, Samir D., Nissanke, Samaya, Pani, Paolo, Platania, Alessia, Pretorius, Frans, Sasaki, Misao, Tiede, Paul, Unruh, William, Visser, Matt, Wald, Robert M.
The gravitational physics landscape is evolving rapidly, driven by our ability to study strong-field regions, in particular black holes. Black Holes Inside and Out gathered world experts to discuss the status of the field and prospects ahead. We hope
Externí odkaz:
http://arxiv.org/abs/2410.14414
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
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
Autonomous indoor navigation of UAVs presents numerous challenges, primarily due to the limited precision of GPS in enclosed environments. Additionally, UAVs' limited capacity to carry heavy or power-intensive sensors, such as overheight packages, ex
Externí odkaz:
http://arxiv.org/abs/2409.03930
Autor:
Sob, Ulrich A. Mbou, Li, Qiulin, Arbesú, Miguel, Bent, Oliver, Smit, Andries P., Pretorius, Arnu
A specific challenge with deep learning approaches for molecule generation is generating both syntactically valid and chemically plausible molecular string representations. To address this, we propose a novel generative latent-variable transformer mo
Externí odkaz:
http://arxiv.org/abs/2407.13780
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:
Chalumeau, Felix, Shabe, Refiloe, De Nicola, Noah, Pretorius, Arnu, Barrett, Thomas D., Grinsztajn, Nathan
Combinatorial Optimization is crucial to numerous real-world applications, yet still presents challenges due to its (NP-)hard nature. Amongst existing approaches, heuristics often offer the best trade-off between quality and scalability, making them
Externí odkaz:
http://arxiv.org/abs/2406.16424
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