Ad hoc teamwork is the challenging problem of designing an autonomous agent which can
adapt quickly to collaborate with teammates without prior coordination mechanisms …

Autonomous agents often need to work together as a team to accomplish complex
cooperative tasks. Due to privacy and other realistic constraints, agents might need to …

In the ad hoc teamwork setting, a team of agents needs to perform a task without prior
coordination. The most advanced approach learns policies based on previous experiences …

This paper investigates the use of model-based reinforcement learning in the context of ad
hoc teamwork. We introduce a novel approach, named TEAMSTER, where we propose …

Robust autonomous agents should be able to cooperate with new teammates effectively by
employing ad hoc teamwork. Reasoning about ad hoc teamwork allows agents to perform …

While great strides have been made in multiagent teamwork, existing approaches typically
assume extensive information exists about teammates and how to coordinate actions. This …

Previous approaches to select agents to form a team rely on single-agent capabilities, and
team performance is treated as a sum of such known capabilities. Motivated by complex …

High-performing teams learn intelligent and efficient communication and coordination
strategies to maximize their joint utility. These teams implicitly understand the different roles …

Recently value-based centralized training with decentralized execution (CTDE) multi-agent
reinforcement learning (MARL) methods have achieved excellent performance in …

In ad hoc teamwork, multiple agents need to collaborate without having knowledge about
their teammates or their plans a priori. A common assumption in this research area is that …