Deep Reinforcement Learning with Hierarchical Structures

被引：0

作者：

Li, Siyuan ^{[1
]}

机构：

[1] Tsinghua Univ, Inst Interdisciplinary Informat Sci, Beijing, Peoples R China

来源：

PROCEEDINGS OF THE THIRTIETH INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE, IJCAI 2021 | 2021年

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：

Hierarchical reinforcement learning (HRL), which enables control at multiple time scales, is a promising paradigm to solve challenging and long-horizon tasks. In this paper, we briefly introduce our work in bottom-up and top-down HRL and outline the directions for future work.

引用

页码：4899 / 4900

页数：2

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