Venue
- IJCAI-2020
Date
- 2021
Share
A Penny for Your Thoughts: The Value of Communication in Ad Hoc Teamwork
Reuth Mirsky*
William Macke*
Andy Wang*
Harel Yedidsion*
* External authors
IJCAI-2020
2021
Abstract
In ad hoc teamwork, multiple agents need to collaborate without having knowledge about their teammates or their plans a priori. A common assumption in this research area is that the agents cannot communicate. However, just as two random people may speak the same language, autonomous teammates may also happen to share a communication protocol. This paper considers how such a shared protocol can be leveraged, introducing a means to reason about Communication in Ad Hoc Teamwork (CAT). The goal of this work is enabling improved ad hoc teamwork by judiciously leveraging the ability of the team to communicate. We situate our study within a novel CAT scenario, involving tasks with multiple steps, where teammates' plans are unveiled over time. In this context, the paper proposes methods to reason about the timing and value of communication and introduces an algorithm for an ad hoc agent to leverage these methods. Finally, we introduces a new multiagent domain, the tool fetching domain, and we study how varying this domain's properties affects the usefulness of communication. Empirical results show the benefits of explicit reasoning about communication content and timing in ad hoc teamwork.
Related Publications
The purpose of continual reinforcement learning is to train an agent on a sequence of tasks such that it learns the ones that appear later in the sequence while retaining theability to perform the tasks that appeared earlier. Experience replay is a popular method used to mak…
When designing reinforcement learning (RL) agents, a designer communicates the desired agent behavior through the definition of reward functions - numerical feedback given to the agent as reward or punishment for its actions. However, mapping desired behaviors to reward func…
Having explored an environment, intelligent agents should be able to transfer their knowledge to most downstream tasks within that environment. Referred to as ``zero-shot learning," this ability remains elusive for general-purpose reinforcement learning algorithms. While rec…
JOIN US
Shape the Future of AI with Sony AI
We want to hear from those of you who have a strong desire
to shape the future of AI.
