Multi-Agent/Robot Deep Reinforcement Learning with Macro-Actions

被引：0

作者：

Xiao, Yuchen ^{[1
]}

Hoffman, Joshua ^{[1
]}

Xia, Tian ^{[1
]}

Amato, Christopher ^{[1
]}

机构：

[1] Northeastern Univ, Khoury Coll Comp Sci, 360 Huntington Ave, Boston, MA 02115 USA

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：

We consider the challenges of learning multi-agent/robot macro-action-based deep Q-nets including how to properly update each macro-action value and accurately maintain macro-action-observation trajectories. We address these challenges by first proposing two fundamental frameworks for learning macro-action-value function and joint macro-action-value function. Furthermore, we present two new approaches of learning decentralized macro-action-based policies, which involve a new double Q-update rule that facilitates the learning of decentralized Q-nets by using a centralized Q-net for action selection. Our approaches are evaluated both in simulation and on real robots.

引用

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页码：13965 / 13966

页数：2

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