Raul D. Steleac — accepted AI-conference papers

2026

Inter-Agent Relative Representations for Multi-Agent Option Discovery

Raul D. Steleac, Mohan Sridharan, David Abel

ICLR 2026poster

Temporally extended actions improve the ability to explore and plan in single-agent settings. In multi-agent settings, the exponential growth of the joint state space with the number of agents makes coordinated behaviours even more valuable. Yet, this same exponential growth renders the design of mu…

Cited by 0 Source ↗Scholar ↗

2024

Scalable Multi-Agent Reinforcement Learning for Warehouse Logistics with Robotic and Human Co-Workers

Aleksandar Krnjaic, Raul D. Steleac, Jonathan D. Thomas, Georgios Papoudakis, Lukas Schäfer, Andrew Wing Keung To +5

IROS 2024poster

We consider a warehouse in which dozens of mobile robots and human pickers work together to collect and deliver items within the warehouse. The fundamental problem we tackle, called the order-picking problem, is how these worker agents must coordinate their movement and actions in the warehouse to m…

Cited by 29 Source ↗code ↗Scholar ↗