The deployment of a fleet of autonomous marine vehicles (AMVs) allows for the
parallelisation of missions, intervehicle support for longer deployment times, adaptability …

This paper considers the challenging tasks of Multi-Agent Reinforcement Learning (MARL)
under partial observability, where each agent only sees her own individual observations and …

Cooperative multi-agent reinforcement learning often requires decentralised policies, which
severely limit the agents' ability to coordinate their behaviour. In this paper, we show that …

In real-world environments, ranging from urban disastrous scenes to underground mining
tunnels, autonomous mobile robots are being deployed in harsh and cluttered …

Decentralized partially observable Markov decision processes (Dec-POMDPs) are general
models for decentralized multi-agent decision making under uncertainty. However, they …

This work presents a strategy for coordinated multi-agent weeding under conditions of
partial environmental information. The goal is to demonstrate the feasibility of coordination …

This paper presents a novel graph reinforcement learning (RL) architecture to solve multi-
robot task allocation (MRTA) problems that involve tasks with deadlines and workload, and …

Today's automated vehicles lack the ability to cooperate implicitly with others. This work
presents a Monte Carlo Tree Search (MCTS) based approach for decentralized cooperative …

In real-world multi-robot systems, performing high-quality, collaborative behaviors requires
robots to asynchronously reason about high-level action selection at varying time durations …

Agent advising is one of the key approaches to improve agent learning performance by
enabling agents to ask for advice between each other. Existing agent advising approaches …