In ad-hoc teamwork, an agent is required to cooperate with unknown teammates without
prior coordination. To swiftly adapt to an unknown teammate, most works adopt a type …

Most approaches to the learning of coordination in multi-agent systems (MAS) to date
require all agents to use the same learning algorithm with similar (or even the same) …

We consider the problem of distributed collaboration among multiple agents in an ad hoc
setting. We have analyzed this problem within a multiagent task execution scenario, in which …

Multi-Agent Reinforcement Learning (MARL) comprises a broad area of research within the
field of multi-agent systems. Several recent works have focused specifically on the study of …

ABSTRACT A mobile sensing robot team (MSRT) is a typical application of multi-agent
systems. This paper investigates multiagent reinforcement learning in the MSRT problem. A …

We study reinforcement learning (RL) in a setting with a network of agents whose states and
actions interact in a local manner where the objective is to find localized policies such that …

Effective coordination is crucial to solve multi-agent collaborative (MAC) problems. While
centralized reinforcement learning methods can optimally solve small MAC instances, they …

Multi-agent reinforcement learning (MARL) has attracted much research attention recently.
However, unlike its single-agent counterpart, many theoretical and algorithmic aspects of …

We study the problem of designing an AI agent that can robustlycooperate with agents of
unknown type (ie, previously unobservedbehavior) in multi-agent scenarios. Our work is …

In this paper we investigate the use of hierarchical reinforcement learning to speed up the
acquisition of cooperative multi-agent tasks. We extend the MAXQ framework to the multi …