Diffusion models have emerged as powerful generative models in the text-to-image domain.
This paper studies their application as observation-to-action models for imitating human …

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 …

Abstract Cooperative Multi-agent Reinforcement Learning (CMARL) has shown to be
promising for many real-world applications. Previous works mainly focus on improving …

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 …

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 …

Cooperative multi-agent reinforcement learning (MARL), where agents coordinates with
teammate (s) for a shared goal, may sustain non-stationary caused by the policy change of …

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 …

Generating agents that can achieve zero-shot coordination (ZSC) with unseen partners is a
new challenge in cooperative multi-agent reinforcement learning (MARL). Recently, some …

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