Cooperation in multi-agent learning (MAL) is a topic at the intersection of numerous
disciplines, including game theory, economics, social sciences, and evolutionary biology …

Multi-agent deep reinforcement learning (MARL) suffers from a lack of commonly-used
evaluation tasks and criteria, making comparisons between approaches difficult. In this work …

The creation and destruction of agents in cooperative multi-agent reinforcement learning
(MARL) is a critically under-explored area of research. Current MARL algorithms often …

Multi-agent Reinforcement Learning (MARL) has gained wide attention in recent years and
has made progress in various fields. Specifically, cooperative MARL focuses on training a …

Ad hoc teamwork is the research problem of designing agents that can collaborate with new
teammates without prior coordination. This survey makes a two-fold contribution: First, it …

Ad hoc teamwork is the well-established research problem of designing agents that can
collaborate with new teammates without prior coordination. This survey makes a two-fold …

Robustly cooperating with unseen agents and human partners presents significant
challenges due to the diverse cooperative conventions these partners may adopt. Existing …

Multi-agent collaboration with Large Language Models (LLMs) demonstrates proficiency in
basic tasks, yet its efficiency in more complex scenarios remains unexplored. In gaming …

Training a robust cooperative agent requires diverse partner agents. However, obtaining
those agents is difficult. Previous works aim to learn diverse behaviors by changing the state …

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 …