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Emergent Resource Exchange and Tolerated Theft Behavior Using Multiagent Reinforcement Learning
Artificial Life ( IF 2.6 ) Pub Date : 2024-02-01 , DOI: 10.1162/artl_a_00423
Jack Garbus 1 , Jordan Pollack 2
Affiliation  

For decades, the evolution of cooperation has piqued interest in numerous academic disciplines, such as game theory, economics, biology, and computer science. In this work, we demonstrate the emergence of a novel and effective resource exchange protocol formed by dropping and picking up resources in a foraging environment. This form of cooperation is made possible by the introduction of a campfire, which adds an extended period of congregation and downtime for agents to explore otherwise unlikely interactions. We find that the agents learn to avoid getting cheated by their exchange partners, but not always from a third party. We also observe the emergence of behavior analogous to tolerated theft, despite the lack of any punishment, combat, or larceny mechanism in the environment.



中文翻译:

使用多代理强化学习的紧急资源交换和容忍盗窃行为

几十年来,合作的演变激起了许多学科的兴趣,例如博弈论、经济学、生物学和计算机科学。在这项工作中,我们展示了一种新颖且有效的资源交换协议的出现,该协议是通过在觅食环境中丢弃和拾取资源而形成的。这种形式的合作是通过篝火的引入而成为可能的,这增加了特工们的聚集时间和停工时间,以探索原本不可能的互动。我们发现,代理学会避免被交换伙伴欺骗,但并不总是来自第三方。我们还观察到类似于容忍盗窃的行为的出现,尽管环境中缺乏任何惩罚、战斗或盗窃机制。

更新日期:2024-02-01
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