2 citations · 3 across the 11 of their papers we have counts for
11 papers
Habermolt: Delegating Deliberation to AI Representatives
Joseph Low, Oscar Duys, Claude Formanek +2
Deliberative democracy arguably leads to better collective decisions, but is fundamentally constrained by human attention and bandwidth. While recent AI-mediated deliberations scal…
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…
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…
Opportunities of Reinforcement Learning in South Africa's Just Transition
Claude Formanek, Callum Rhys Tilbury, Jonathan P. Shock
South Africa stands at a crucial juncture, grappling with interwoven socio-economic challenges such as poverty, inequality, unemployment, and the looming climate crisis. The govern…
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…
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…
Selective Reincarnation: Offline-to-Online Multi-Agent Reinforcement Learning
Claude Formanek, Callum Rhys Tilbury, Jonathan Shock +2
'Reincarnation' in reinforcement learning has been proposed as a formalisation of reusing prior computation from past experiments when training an agent in an environment. In this…
Off-the-Grid MARL: Datasets with Baselines for Offline Multi-Agent Reinforcement Learning
Claude Formanek, Asad Jeewa, Jonathan Shock +1
Being able to harness the power of large datasets for developing cooperative multi-agent controllers promises to unlock enormous value for real-world applications. Many important i…
Mava: a research library for distributed multi-agent reinforcement learning in JAX
Ruan de Kock, Omayma Mahjoub, Sasha Abramowitz +5
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 hyp…