Gifting in Multi-Agent Reinforcement Learning (Student Abstract)

被引：0

作者：

Lupu, Andrei ^{[1
]}

Precup, Doina ^{[1
]}

机构：

[1] McGill Univ, Mila, 3480 Univ St, Montreal, PQ H3A 2A7, Canada

来源：

THIRTY-FOURTH AAAI CONFERENCE ON ARTIFICIAL INTELLIGENCE, THE THIRTY-SECOND INNOVATIVE APPLICATIONS OF ARTIFICIAL INTELLIGENCE CONFERENCE AND THE TENTH AAAI SYMPOSIUM ON EDUCATIONAL ADVANCES IN ARTIFICIAL INTELLIGENCE | 2020年 / 34卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This work performs a first study on multi-agent reinforcement learning with deliberate reward passing between agents. We empirically demonstrate that such mechanics can greatly improve the learning progression in a resource appropriation setting and provide a preliminary discussion of the complex effects of gifting on the learning dynamics.

引用

页码：13871 / 13872

页数：2

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