3 papers
cs.LG2025
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…
cs.CY2024
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…
cs.LG2024
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…