Zobrazeno 1 - 9
of 9
pro vyhledávání: '"de Kock, Ruan"'
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:
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
Autor:
Khlifi, Wiem, Singh, Siddarth, Mahjoub, Omayma, de Kock, Ruan, Vall, Abidine, Gorsane, Rihab, Pretorius, Arnu
Cooperative multi-agent reinforcement learning (MARL) has made substantial strides in addressing the distributed decision-making challenges. However, as multi-agent systems grow in complexity, gaining a comprehensive understanding of their behaviour
Externí odkaz:
http://arxiv.org/abs/2312.08468
Autor:
Mahjoub, Omayma, de Kock, Ruan, Singh, Siddarth, Khlifi, Wiem, Vall, Abidine, Tessera, Kale-ab, Pretorius, Arnu
Measuring the contribution of individual agents is challenging in cooperative multi-agent reinforcement learning (MARL). In cooperative MARL, team performance is typically inferred from a single shared global reward. Arguably, among the best current
Externí odkaz:
http://arxiv.org/abs/2312.08466
Autor:
Singh, Siddarth, Mahjoub, Omayma, de Kock, Ruan, Khlifi, Wiem, Vall, Abidine, Tessera, Kale-ab, Pretorius, Arnu
Establishing sound experimental standards and rigour is important in any growing field of research. Deep Multi-Agent Reinforcement Learning (MARL) is one such nascent field. Although exciting progress has been made, MARL has recently come under scrut
Externí odkaz:
http://arxiv.org/abs/2312.08463
Autor:
Tessera, Kale-ab, Tilbury, Callum Rhys, Abramowitz, Sasha, de Kock, Ruan, Mahjoub, Omayma, Rosman, Benjamin, Hooker, Sara, Pretorius, Arnu
Optimising deep neural networks is a challenging task due to complex training dynamics, high computational requirements, and long training times. To address this difficulty, we propose the framework of Generalisable Agents for Neural Network Optimisa
Externí odkaz:
http://arxiv.org/abs/2311.18598
Autor:
Bonnet, Clément, Luo, Daniel, Byrne, Donal, Surana, Shikha, Abramowitz, Sasha, Duckworth, Paul, Coyette, Vincent, Midgley, Laurence I., Tegegn, Elshadai, Kalloniatis, Tristan, Mahjoub, Omayma, Macfarlane, Matthew, Smit, Andries P., Grinsztajn, Nathan, Boige, Raphael, Waters, Cemlyn N., Mimouni, Mohamed A., Sob, Ulrich A. Mbou, de Kock, Ruan, Singh, Siddarth, Furelos-Blanco, Daniel, Le, Victor, Pretorius, Arnu, Laterre, Alexandre
Open-source reinforcement learning (RL) environments have played a crucial role in driving progress in the development of AI algorithms. In modern RL research, there is a need for simulated environments that are performant, scalable, and modular to e
Externí odkaz:
http://arxiv.org/abs/2306.09884
Autor:
Gorsane, Rihab, Mahjoub, Omayma, de Kock, Ruan, Dubb, Roland, Singh, Siddarth, Pretorius, Arnu
Multi-agent reinforcement learning (MARL) has emerged as a useful approach to solving decentralised decision-making problems at scale. Research in the field has been growing steadily with many breakthrough algorithms proposed in recent years. In this
Externí odkaz:
http://arxiv.org/abs/2209.10485
Autor:
de Kock, Ruan, Mahjoub, Omayma, Abramowitz, Sasha, Khlifi, Wiem, Tilbury, Callum Rhys, Formanek, Claude, Smit, Andries, Pretorius, Arnu
Multi-agent reinforcement learning (MARL) research is inherently computationally expensive and it is often difficult to obtain a sufficient number of experiment samples to test hypotheses and make robust statistical claims. Furthermore, MARL algorith
Externí odkaz:
http://arxiv.org/abs/2107.01460