Learning to Participate through Trading of Reward Shares

Enabling autonomous agents to act cooperatively is an important step to integrate artificial intelligence in our daily lives. While some methods seek to stimulate cooperation by letting agents give rewards to others, in this paper we propose a method inspired by the stock market, where agents have t...

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Bibliographic Details
Published inarXiv.org
Main Authors Kölle, Michael, Matheis, Tim, Altmann, Philipp, Schmid, Kyrill
Format Paper Journal Article
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 18.01.2023
Subjects
Agents (artificial intelligence)
Artificial intelligence
Computer Science - Artificial Intelligence
Computer Science - Multiagent Systems
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Summary:Enabling autonomous agents to act cooperatively is an important step to integrate artificial intelligence in our daily lives. While some methods seek to stimulate cooperation by letting agents give rewards to others, in this paper we propose a method inspired by the stock market, where agents have the opportunity to participate in other agents' returns by acquiring reward shares. Intuitively, an agent may learn to act according to the common interest when being directly affected by the other agents' rewards. The empirical results of the tested general-sum Markov games show that this mechanism promotes cooperative policies among independently trained agents in social dilemma situations. Moreover, as demonstrated in a temporally and spatially extended domain, participation can lead to the development of roles and the division of subtasks between the agents.
ISSN:2331-8422
DOI:10.48550/arxiv.2301.07416