首页> Computational economics> Computational Performance of Deep Reinforcement Learning to Find Nash Equilibria.

Computational Performance of Deep Reinforcement Learning to Find Nash Equilibria.

导出 LinkOut

翻译打印收藏纠错

第一作者： Christoph,Graf

第一单位： Institute for Policy Integrity, New York University, New York, NY 10012 USA.;Program on Energy and Sustainable Development (PESD), Stanford University, Stanford, CA 94305-6072 USA.

作者： Christoph,Graf ^[1] ; Viktor,Zobernig ^[2] ; Johannes,Schmidt ^[2] ; Claude,Klöckl ^[2]

作者单位： Institute for Policy Integrity, New York University, New York, NY 10012 USA.;Program on Energy and Sustainable Development (PESD), Stanford University, Stanford, CA 94305-6072 USA. ^[1] Institute for Sustainable Economic Development, University of Natural Resources and Life Sciences, Vienna, Austria. ^[2]

关键词 Bertrand equilibrium Competition in uniform price auctions DDPG Deep deterministic policy gradient algorithm Parameter sensitivity analysis

DOI 10.1007/s10614-022-10351-6

PMID 38304891

发布时间 2024-02-03

翻译满意度评价：

准确一般字义偏差语意偏离译文作用小提交

浏览0

Computational economics

2024年63卷2期

529-576页

相似文献

中文期刊
外文期刊
学位论文
会议论文

浏览0

点击下载本期目录封面

Computational economics

2024年63卷2期

529-576页

检索历史清除

Computational Performance of Deep Reinforcement Learning to Find Nash Equilibria.

Computational economics

相似文献

同项目论文

Computational economics

相关期刊

参考文献：
指定格式：	NoteExpress EndNote RefWorks NoteFirst

检索历史 清除

Computational Performance of Deep Reinforcement Learning to Find Nash Equilibria.

Computational economics

相似文献

同项目论文

Computational economics

相关期刊

检索历史清除