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 …

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

In many real‐world applications of AI, the set of actors and tasks are not constant, but
instead change over time. Robots tasked with suppressing wildfires eventually run out of …

Effective human-human and human-autonomy teamwork is critical but often challenging to
perfect. The challenge is particularly relevant in time-critical domains, such as healthcare …

The conversational recommendation system (CRS) has been criticized regarding its user
experience in real-world scenarios, despite recent significant progress achieved in …

Open ad hoc teamwork is the problem of training a single agent to efficiently collaborate with
an unknown group of teammates whose composition may change over time. A variable team …

In this study, we explore the robustness of cooperative multi-agent reinforcement learning (c-
MARL) against Byzantine failures, where any agent can enact arbitrary, worst-case actions …

Ad hoc teamwork requires an agent to cooperate with unknown teammates without prior
coordination. Many works propose to abstract teammate instances into high-level …

Training a team to complete a complex task via multi-agent reinforcement learning can be
difficult due to challenges such as policy search in a large policy space, and non-stationarity …

Research on multi-agent interaction involving both multiple artificial agents and humans is
still in its infancy. Most recent approaches have focused on environments with collaboration …