Cooperative Multi-Agent Deep Reinforcement Learning in Soccer Domains

被引：0

作者：

Ocana, Jim Martin Catacora ^{[1
]}

Riccio, Francesco ^{[1
]}

Capobianco, Roberto ^{[1
]}

Nardi, Daniele ^{[1
]}

机构：

[1] Sapienza Univ Rome, Rome, Italy

来源：

AAMAS '19: PROCEEDINGS OF THE 18TH INTERNATIONAL CONFERENCE ON AUTONOMOUS AGENTS AND MULTIAGENT SYSTEMS | 2019年

关键词：

Multi-Robot; Deep Reinforcement Learning; Robot Soccer;

D O I：

暂无

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

In multi-robot reinforcement learning the goal is to enable a group of robots to learn coordinated behaviors from direct interaction with the environment. Here, we provide a comparison of two main approaches designed for tackling this challenge; namely, independent learners (IL) and joint-action learners (JAL). We evaluate these methods in a multi-robot cooperative and adversarial soccer scenario, called 2 versus 2 free-kick task, with simulated NAO humanoid robots as players. Our findings show that both approaches can achieve satisfying solutions, with JAL outperforming IL.

引用

页码：1865 / 1867

页数：3

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