5 citations · 5 across the 3 of their papers we have counts for
5 papers
Characterizing MARL for Energy Control: A Multi-KPI Benchmark on the CityLearn Environment
Aymen Khouja, Imen Jendoubi, Oumayma Mahjoub +4
The optimization of urban energy systems is crucial for the advancement of sustainable and resilient smart cities, which are becoming increasingly complex with multiple decision-ma…
Oryx: a Scalable Sequence Model for Many-Agent Coordination in Offline MARL
Claude Formanek, Omayma Mahjoub, Louay Ben Nessir +10
A key challenge in offline multi-agent reinforcement learning (MARL) is achieving effective many-agent multi-step coordination in complex environments. In this work, we propose Ory…
Multi-Agent Reinforcement Learning with Selective State-Space Models
Jemma Daniel, Ruan de Kock, Louay Ben Nessir +5
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 em…
Sable: a Performant, Efficient and Scalable Sequence Model for MARL
Omayma Mahjoub, Sasha Abramowitz, Ruan de Kock +8
As multi-agent reinforcement learning (MARL) progresses towards solving larger and more complex problems, it becomes increasingly important that algorithms exhibit the key properti…
Towards a Standardised Performance Evaluation Protocol for Cooperative MARL
Rihab Gorsane, Omayma Mahjoub, Ruan de Kock +3
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 steadi…