Building agents with adaptive behavior in cooperative tasks stands as a paramount goal in
the realm of multi-agent systems. Current approaches to developing cooperative agents rely …

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 …

The difficulty of appropriately assigning credit is particularly heightened in cooperative
MARL with sparse reward, due to the concurrent time and structural scales involved …

Building agents with adaptive behavior in cooperative tasks stands as a paramount goal in
the realm of multi-agent systems. Current approaches to developing cooperative agents rely …

Cognitive scientists believe that adaptable intelligent agents like humans perform spatial
reasoning tasks by learned causal mental simulation. The problem of learning these …

Generalizing policies learned by agents in known environments to unseen domains is an
essential challenge in advancing the development of reinforcement learning. Lately …

Large language models (LLMs) have assumed an increasingly crucial role in robotic
systems because of their ability to leverage the extensive knowledge they possess in robotic …

Cooperative multi-agent reinforcement learning (MARL) presents unique challenges,
amongst which fostering general cooperative behaviour across various tasks is critical …

Exploration is one of the central topic in reinforcement learning (RL). Many existing
approaches take a single agent perspective when tackling this problem. In this work, we …