6 papers
Scaling few-shot spoken word classification with generative meta-continual learning
Louise Beyers, Batsirayi Mupamhi Ziki, Ruan van der Merwe
Few-shot spoken word classification has largely been developed for applications where a small number of classes is considered, and so the potential of larger-scale few-shot spoken…
Does language matter for spoken word classification? A multilingual generative meta-learning approach
Batsirayi Mupamhi Ziki, Louise Beyers, Ruan van der Merwe
Meta-learning has been shown to have better performance than supervised learning for few-shot monolingual spoken word classification. However, the meta-learning approach remains un…
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…
Putting Data at the Centre of Offline Multi-Agent Reinforcement Learning
Claude Formanek, Louise Beyers, Callum Rhys Tilbury +2
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 th…
Coordination Failure in Cooperative Offline MARL
Callum Rhys Tilbury, Claude Formanek, Louise Beyers +2
Offline multi-agent reinforcement learning (MARL) leverages static datasets of experience to learn optimal multi-agent control. However, learning from static data presents several…
Dispelling the Mirage of Progress in Offline MARL through Standardised Baselines and Evaluation
Claude Formanek, Callum Rhys Tilbury, Louise Beyers +2
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…