8 papers
Out-of-Distribution Generalisation with Sequence Models in Offline Multi-Agent Reinforcement Learning
Oussama Hidaoui, Omer Ebead, Ulrich Armel Mbou Sob +14
Generalising to unseen tasks remains a fundamental challenge in offline multi-agent reinforcement learning (MARL). In this work, we present a principled analysis of zero-shot task…
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…
Breaking the Performance Ceiling in Reinforcement Learning requires Inference Strategies
Felix Chalumeau, Daniel Rajaonarivonivelomanantsoa, Ruan de Kock +12
Reinforcement learning (RL) systems have countless applications, from energy-grid management to protein design. However, such real-world scenarios are often extremely difficult, co…
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…
Efficiently Quantifying Individual Agent Importance in Cooperative MARL
Omayma Mahjoub, Ruan de Kock, Siddarth Singh +4
Measuring the contribution of individual agents is challenging in cooperative multi-agent reinforcement learning (MARL). In cooperative MARL, team performance is typically inferred…
How much can change in a year? Revisiting Evaluation in Multi-Agent Reinforcement Learning
Siddarth Singh, Omayma Mahjoub, Ruan de Kock +4
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. Althou…
On Diagnostics for Understanding Agent Training Behaviour in Cooperative MARL
Wiem Khlifi, Siddarth Singh, Omayma Mahjoub +4
Cooperative multi-agent reinforcement learning (MARL) has made substantial strides in addressing the distributed decision-making challenges. However, as multi-agent systems grow in…