5 papers
Self-Supervised On-Policy Reinforcement Learning via Contrastive Proximal Policy Optimisation
Asim Osman, Sasha Abramowitz, Mark Bergh +13
Contrastive reinforcement learning (CRL) learns goal-conditioned Q-values through a contrastive objective over state-action and goal representations, removing the need for hand-cra…
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…
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…
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…
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…