Collaborating with humans requires rapidly adapting to their individual strengths,
weaknesses, and preferences. Unfortunately, most standard multi-agent reinforcement …

The key challenge in multiagent learning is learning a best response to the behaviour of
other agents, which may be non-stationary: if the other agents adapt their strategy as well …

Collaboration requires agents to coordinate their behavior on the fly, sometimes cooperating
to solve a single task together and other times dividing it up into sub‐tasks to work on in …

Ad hoc teamwork is the research problem of designing agents that can collaborate with new
teammates without prior coordination. This survey makes a two-fold contribution: First, it …

近年来, 深度强化学习(Deep reinforcement learning, DRL) 在诸多复杂序贯决策问题中取得
巨大突破. 由于融合了深度学习强大的表征能力和强化学习有效的策略搜索能力 …

Ad hoc teamwork is the well-established research problem of designing agents that can
collaborate with new teammates without prior coordination. This survey makes a two-fold …

This paper investigates the use of model-based reinforcement learning in the context of ad
hoc teamwork. We introduce a novel approach, named TEAMSTER, where we propose …

© 2020 International Foundation for Autonomous Agents and Multiagent Systems
(IFAAMAS). All rights reserved. Humans collaborate in dynamic and flexible ways …

In the ad hoc teamwork setting, a team of agents needs to perform a task without prior
coordination. The most advanced approach learns policies based on previous experiences …

The development of autonomous agents which can interact with other agents to accomplish
a given task is a core area of research in artificial intelligence and machine learning …