JP2018526733A5 - - Google Patents

Download PDF

Info

Publication number
JP2018526733A5
JP2018526733A5 JP2018504858A JP2018504858A JP2018526733A5 JP 2018526733 A5 JP2018526733 A5 JP 2018526733A5 JP 2018504858 A JP2018504858 A JP 2018504858A JP 2018504858 A JP2018504858 A JP 2018504858A JP 2018526733 A5 JP2018526733 A5 JP 2018526733A5
Authority
JP
Japan
Prior art keywords
network
action
parameter
target
current value
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Granted
Application number
JP2018504858A
Other languages
English (en)
Japanese (ja)
Other versions
JP7080811B2 (ja
JP2018526733A (ja
Filing date
Publication date
Application filed filed Critical
Priority claimed from PCT/US2016/051095 external-priority patent/WO2017044842A1/en
Publication of JP2018526733A publication Critical patent/JP2018526733A/ja
Publication of JP2018526733A5 publication Critical patent/JP2018526733A5/ja
Application granted granted Critical
Publication of JP7080811B2 publication Critical patent/JP7080811B2/ja
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

JP2018504858A 2015-09-11 2016-09-09 強化学習ニューラルネットワークのトレーニング Active JP7080811B2 (ja)

Applications Claiming Priority (3)

Application Number Priority Date Filing Date Title
US201562217547P 2015-09-11 2015-09-11
US62/217,547 2015-09-11
PCT/US2016/051095 WO2017044842A1 (en) 2015-09-11 2016-09-09 Training reinforcement learning neural networks

Publications (3)

Publication Number Publication Date
JP2018526733A JP2018526733A (ja) 2018-09-13
JP2018526733A5 true JP2018526733A5 (enExample) 2020-10-01
JP7080811B2 JP7080811B2 (ja) 2022-06-06

Family

ID=56979671

Family Applications (1)

Application Number Title Priority Date Filing Date
JP2018504858A Active JP7080811B2 (ja) 2015-09-11 2016-09-09 強化学習ニューラルネットワークのトレーニング

Country Status (6)

Country Link
US (2) US10733504B2 (enExample)
EP (1) EP3295384B1 (enExample)
JP (1) JP7080811B2 (enExample)
KR (1) KR102140672B1 (enExample)
CN (1) CN107851216B (enExample)
WO (1) WO2017044842A1 (enExample)

Families Citing this family (48)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102015213190B3 (de) 2015-07-14 2016-09-29 Bayerische Motoren Werke Aktiengesellschaft Längsführendes Fahrerassistenzsystem in einem Kraftfahrzeug
DE102015213183A1 (de) 2015-07-14 2017-01-19 Bayerische Motoren Werke Aktiengesellschaft Längsführendes Fahrerassistenzsystem in einem Kraftfahrzeug
DE102015213181B3 (de) * 2015-07-14 2017-01-19 Bayerische Motoren Werke Aktiengesellschaft Längsführendes Fahrerassistenzsystem in einem Kraftfahrzeug
US10885432B1 (en) * 2015-12-16 2021-01-05 Deepmind Technologies Limited Selecting actions from large discrete action sets using reinforcement learning
CN117521725A (zh) * 2016-11-04 2024-02-06 渊慧科技有限公司 加强学习系统
KR102399535B1 (ko) * 2017-03-23 2022-05-19 삼성전자주식회사 음성 인식을 위한 학습 방법 및 장치
US11263490B2 (en) 2017-04-07 2022-03-01 Intel Corporation Methods and systems for budgeted and simplified training of deep neural networks
WO2018189404A1 (en) * 2017-04-14 2018-10-18 Deepmind Technologies Limited Distributional reinforcement learning
US10396919B1 (en) * 2017-05-12 2019-08-27 Virginia Tech Intellectual Properties, Inc. Processing of communications signals using machine learning
KR102391452B1 (ko) * 2017-06-02 2022-04-27 에스케이텔레콤 주식회사 신경망에 복수의 태스크를 순차 학습시키는 방법
US11604997B2 (en) * 2017-06-09 2023-03-14 Deepmind Technologies Limited Training action selection neural networks using leave-one-out-updates
US10678241B2 (en) * 2017-09-06 2020-06-09 GM Global Technology Operations LLC Unsupervised learning agents for autonomous driving applications
US10254759B1 (en) * 2017-09-14 2019-04-09 Waymo Llc Interactive autonomous vehicle agent
EP3616130B1 (en) * 2017-09-20 2024-04-10 Google LLC Using simulation and domain adaptation for robotic control
EP3467718A1 (en) 2017-10-04 2019-04-10 Prowler.io Limited Machine learning system
EP3467717A1 (en) * 2017-10-04 2019-04-10 Prowler.io Limited Machine learning system
CN109726811A (zh) 2017-10-27 2019-05-07 谷歌有限责任公司 使用优先级队列训练神经网络
US11568236B2 (en) 2018-01-25 2023-01-31 The Research Foundation For The State University Of New York Framework and methods of diverse exploration for fast and safe policy improvement
DK3698291T3 (da) 2018-02-05 2025-02-10 Deepmind Tech Ltd Distribueret træning ved anvendelse af off-policyaktørkritisk forstærkningslæring
FR3077666B1 (fr) * 2018-02-05 2020-01-17 Psa Automobiles Sa Procede de configuration automatique d’un generateur de recompenses utilisees par des strategies de conduite d’un vehicule a conduite automatisee
EP3701432B1 (en) * 2018-02-09 2025-04-02 DeepMind Technologies Limited Distributional reinforcement learning using quantile function neural networks
US12221101B2 (en) 2018-03-04 2025-02-11 Traxen Inc. Automated cruise control system
CN112154461B (zh) * 2018-05-18 2025-02-25 渊慧科技有限公司 用于多代理环境中的行为预测和强化学习的图神经网络系统
EP4357976A1 (en) * 2018-05-29 2024-04-24 DeepMind Technologies Limited Deep reinforcement learning with fast updating recurrent neural networks and slow updating recurrent neural
US11188035B2 (en) 2018-07-19 2021-11-30 International Business Machines Corporation Continuous control of attention for a deep learning network
CN109952582B (zh) * 2018-09-29 2023-07-14 区链通网络有限公司 一种强化学习模型的训练方法、节点、系统及存储介质
US10789511B2 (en) * 2018-10-12 2020-09-29 Deepmind Technologies Limited Controlling agents over long time scales using temporal value transport
CN111105029B (zh) * 2018-10-29 2024-04-16 北京地平线机器人技术研发有限公司 神经网络的生成方法、生成装置和电子设备
CN109598332B (zh) * 2018-11-14 2021-04-09 北京市商汤科技开发有限公司 神经网络生成方法及装置、电子设备和存储介质
CN113574547B (zh) * 2019-03-20 2024-01-19 索尼集团公司 通过双演员评论家算法进行强化学习
US11373108B2 (en) * 2019-07-10 2022-06-28 Microsoft Technology Licensing, Llc Reinforcement learning in real-time communications
KR20210012730A (ko) 2019-07-26 2021-02-03 삼성전자주식회사 인공지능 모델의 학습 방법 및 전자 장치
EP4014165A1 (en) * 2019-09-13 2022-06-22 DeepMind Technologies Limited Data-driven robot control
US20210158196A1 (en) * 2019-11-25 2021-05-27 Deepmind Technologies Limited Non-stationary delayed bandits with intermediate signals
CN111046156B (zh) * 2019-11-29 2023-10-13 支付宝(杭州)信息技术有限公司 奖励数据的确定方法、装置和服务器
US11514274B2 (en) * 2020-03-30 2022-11-29 Google Llc Geographic dataset preparation system
CN113877202B (zh) * 2020-07-01 2025-04-29 中移(苏州)软件技术有限公司 一种游戏控制方法及装置、存储介质
US12450534B2 (en) * 2020-07-20 2025-10-21 Georgia Tech Research Corporation Heterogeneous graph attention networks for scalable multi-robot scheduling
CN112476424B (zh) * 2020-11-13 2025-05-09 腾讯科技（深圳）有限公司 机器人控制方法、装置、设备及计算机存储介质
CN112580801B (zh) * 2020-12-09 2021-10-15 广州优策科技有限公司 一种强化学习训练方法及基于强化学习的决策方法
KR102388911B1 (ko) * 2021-03-08 2022-04-21 (주)뤼이드 데이터 증강 기반 지식 추적 모델 학습 장치, 시스템 및 그것의 동작방법
DE102021107458A1 (de) 2021-03-25 2022-09-29 Dr. Ing. H.C. F. Porsche Aktiengesellschaft Steuervorrichtung und Verfahren
CN113268933B (zh) * 2021-06-18 2022-02-15 大连理工大学 基于强化学习的蛇形急救机器人结构参数快速设计方法
CN113361132B (zh) * 2021-06-28 2022-03-15 浩鲸云计算科技股份有限公司 一种基于深度q学习对决网络的风冷数据中心节能方法
US20220274251A1 (en) * 2021-11-12 2022-09-01 Intel Corporation Apparatus and methods for industrial robot code recommendation
CN115114030B (zh) * 2022-07-20 2023-06-16 杭州电子科技大学 一种基于强化学习的在线多工作流调度方法
US20240143995A1 (en) * 2022-10-31 2024-05-02 Gm Cruise Holdings Llc Determining a distribution for a neural network architecture
CN116663652B (zh) * 2023-04-20 2025-10-10 华中师范大学 一种强化学习支持下的协作学习干预方法及系统

Family Cites Families (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US5790754A (en) * 1994-10-21 1998-08-04 Sensory Circuits, Inc. Speech recognition apparatus for consumer electronic applications
EP1444600A1 (en) * 2001-11-16 2004-08-11 Yuan Yan Chen Pausible neural network with supervised and unsupervised cluster analysis
CN101261634B (zh) * 2008-04-11 2012-11-21 哈尔滨工业大学深圳研究生院 基于增量Q-Learning的学习方法及系统
CN102195975A (zh) * 2011-04-08 2011-09-21 上海电机学院 基于移动代理和学习向量量化神经网络的智能nips架构
CN102573055B (zh) * 2011-12-02 2014-04-09 上海电机学院 一种无线传感网络中的节点定位方法及系统
US8788439B2 (en) 2012-12-21 2014-07-22 InsideSales.com, Inc. Instance weighted learning machine learning model
JP5997092B2 (ja) * 2013-04-17 2016-09-28 日本電信電話株式会社 ロボット協調搬送計画装置、方法及びプログラム
US9286573B2 (en) 2013-07-17 2016-03-15 Xerox Corporation Cost-aware non-stationary online learning
US9679258B2 (en) * 2013-10-08 2017-06-13 Google Inc. Methods and apparatus for reinforcement learning
JP6230052B2 (ja) * 2013-10-11 2017-11-15 株式会社オーディオテクニカ エレクトレットコンデンサマイクロホン
JP2015089801A (ja) * 2013-11-07 2015-05-11 株式会社デンソー 運転制御装置
EP3204888A4 (en) * 2014-10-09 2017-10-04 Microsoft Technology Licensing, LLC Spatial pyramid pooling networks for image processing
CN104794359B (zh) * 2015-04-29 2017-12-15 电子科技大学 一种迭代步长可变的多步q学习自适应方法

Similar Documents

Publication Publication Date Title
JP2018526733A5 (enExample)
US11886992B2 (en) Training reinforcement learning neural networks
US11783182B2 (en) Asynchronous deep reinforcement learning
JP6963627B2 (ja) 畳み込みニューラルネットワークのためのニューラルアーキテクチャ検索
US20230237314A1 (en) Image processing neural networks with separable convolutional layers
US10832124B2 (en) Generating natural language descriptions of images
US10762421B2 (en) Whitened neural network layers
US10445641B2 (en) Distributed training of reinforcement learning systems
US20200279162A1 (en) Selecting action slates using reinforcement learning
EP4386624A3 (en) Environment navigation using reinforcement learning
EP3079106A2 (en) Selecting reinforcement learning actions using goals and observations
CN118246513A (zh) 训练动作选择神经网络
WO2018125337A3 (en) Automated generation of workflows
JP2019511033A5 (enExample)
JP2017049427A5 (enExample)
WO2017091629A1 (en) Reinforcement learning using confidence scores
JP2016503216A5 (enExample)
JP2015511032A5 (ja) ソフトウエアを開発するためのコンピュータプログラム、方法、コンピュータシステム、装置及びコンピュータ
RU2015143745A (ru) Способ и сервер проведения контролируемого эксперимента с использованием прогнозирования будущего пользовательского поведения
JP2015114988A5 (enExample)
CN105095202B (zh) 消息推荐方法及装置
JP2018106237A5 (enExample)
Yoo The Bayesian method for causal discovery of latent-variable models from a mixture of experimental and observational data
JP2013152595A5 (enExample)
JP2010146337A5 (enExample)