JP7468619B2 - 学習装置、学習方法、及び、記録媒体 - Google Patents

学習装置、学習方法、及び、記録媒体 Download PDF

Info

Publication number
JP7468619B2
JP7468619B2 JP2022508616A JP2022508616A JP7468619B2 JP 7468619 B2 JP7468619 B2 JP 7468619B2 JP 2022508616 A JP2022508616 A JP 2022508616A JP 2022508616 A JP2022508616 A JP 2022508616A JP 7468619 B2 JP7468619 B2 JP 7468619B2
Authority
JP
Japan
Prior art keywords
learning
policy
difficulty level
target system
control
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.)
Active
Application number
JP2022508616A
Other languages
English (en)
Japanese (ja)
Other versions
JPWO2021186500A5 (enExample
JPWO2021186500A1 (enExample
Inventor
卓磨 向後
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
NEC Corp
Original Assignee
NEC Corp
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by NEC Corp filed Critical NEC Corp
Publication of JPWO2021186500A1 publication Critical patent/JPWO2021186500A1/ja
Publication of JPWO2021186500A5 publication Critical patent/JPWO2021186500A5/ja
Application granted granted Critical
Publication of JP7468619B2 publication Critical patent/JP7468619B2/ja
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • GPHYSICS
    • G06COMPUTING OR CALCULATING; COUNTING
    • G06NCOMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00Machine learning

Landscapes

  • Engineering & Computer Science (AREA)
  • Theoretical Computer Science (AREA)
  • Software Systems (AREA)
  • Data Mining & Analysis (AREA)
  • Evolutionary Computation (AREA)
  • Medical Informatics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Physics & Mathematics (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Artificial Intelligence (AREA)
  • Feedback Control In General (AREA)
JP2022508616A 2020-03-16 2020-03-16 学習装置、学習方法、及び、記録媒体 Active JP7468619B2 (ja)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/JP2020/011465 WO2021186500A1 (ja) 2020-03-16 2020-03-16 学習装置、学習方法、及び、記録媒体

Publications (3)

Publication Number Publication Date
JPWO2021186500A1 JPWO2021186500A1 (enExample) 2021-09-23
JPWO2021186500A5 JPWO2021186500A5 (enExample) 2022-11-08
JP7468619B2 true JP7468619B2 (ja) 2024-04-16

Family

ID=77770726

Family Applications (1)

Application Number Title Priority Date Filing Date
JP2022508616A Active JP7468619B2 (ja) 2020-03-16 2020-03-16 学習装置、学習方法、及び、記録媒体

Country Status (3)

Country Link
US (1) US20240202569A1 (enExample)
JP (1) JP7468619B2 (enExample)
WO (1) WO2021186500A1 (enExample)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114357884B (zh) * 2022-01-05 2022-11-08 厦门宇昊软件有限公司 一种基于深度强化学习的反应温度控制方法和系统
CN114404977B (zh) * 2022-01-25 2024-04-16 腾讯科技(深圳)有限公司 行为模型的训练方法、结构扩容模型的训练方法
CN119249911B (zh) * 2024-12-03 2025-04-04 西北工业大学 一种基于迁移学习的流动主动控制增效设计方法

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2017183587A1 (ja) 2016-04-18 2017-10-26 日本電信電話株式会社 学習装置、学習方法および学習プログラム
JP2019219741A (ja) 2018-06-15 2019-12-26 株式会社日立製作所 学習制御方法及び計算機システム

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2017183587A1 (ja) 2016-04-18 2017-10-26 日本電信電話株式会社 学習装置、学習方法および学習プログラム
JP2019219741A (ja) 2018-06-15 2019-12-26 株式会社日立製作所 学習制御方法及び計算機システム

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
JIANG, Lu ほか,Self-Paced Learning with Diversity,Advances in Neural Information Processing Systems 27(NIPS 2014)[online],Neural Information Processing Systems Foundation,2014年,pp.1-9,[retrieved on 2020.07.27], Retrieved from the Internet: <URL: https://papers.nips.cc/paper/5568-self-paced-learning-with-diversity.pdf>

Also Published As

Publication number Publication date
WO2021186500A1 (ja) 2021-09-23
JPWO2021186500A1 (enExample) 2021-09-23
US20240202569A1 (en) 2024-06-20

Similar Documents

Publication Publication Date Title
US12162150B2 (en) Learning method, learning apparatus, and learning system
JP7468619B2 (ja) 学習装置、学習方法、及び、記録媒体
JP6911798B2 (ja) ロボットの動作制御装置
WO2020065001A1 (en) Learning motor primitives and training a machine learning system using a linear-feedback-stabilized policy
JP7379833B2 (ja) 強化学習方法、強化学習プログラム、および強化学習システム
KR20220137732A (ko) 적응형 리턴 계산 방식을 사용한 강화 학습
EP3704550B1 (en) Generation of a control system for a target system
KR101912918B1 (ko) 학습 로봇, 그리고 이를 이용한 작업 솜씨 학습 방법
CN114529010B (zh) 一种机器人自主学习方法、装置、设备及存储介质
KR20230057673A (ko) 딥 러닝 기반의 반도체 소자의 특성 예측 방법 및 이를 수행하는 컴퓨팅 장치
CN114502338B (zh) 用于生成机器人的控制器的技术
Ting et al. Locally weighted regression for control
EP4528406A1 (en) Techniques for controlling robots using dynamic gain tuning
Gao Optimizing robotic arm control using deep Q-learning and artificial neural networks through demonstration-based methodologies: A case study of dynamic and static conditions
Ma et al. A novel APSO-aided weighted LSSVM method for nonlinear hammerstein system identification
Qazani et al. Whale optimization algorithm for weight tuning of a model predictive control-based motion cueing algorithm
JP7647862B2 (ja) 学習装置、学習方法及びプログラム
WO2019216427A1 (ja) リスク指標評価装置、リスク指標評価方法及びプログラム
Chen et al. ClutterDexGrasp: A Sim-to-Real System for General Dexterous Grasping in Cluttered Scenes
Xu et al. Deep reinforcement learning for parameter tuning of robot visual servoing
JP2020179438A (ja) 計算機システム及び機械学習方法
JP7579632B2 (ja) 推定装置、システム及び方法
Mainampati et al. Implementation of human in the loop on the TurtleBot using reinforced learning methods and robot operating system (ROS)
CN116277016A (zh) 机械臂速度控制方法、装置、计算机设备及存储介质
Li et al. Shaping Sparse Rewards in Reinforcement Learning: A Semi-supervised Approach

Legal Events

Date Code Title Description
A521 Request for written amendment filed

Free format text: JAPANESE INTERMEDIATE CODE: A523

Effective date: 20220907

A621 Written request for application examination

Free format text: JAPANESE INTERMEDIATE CODE: A621

Effective date: 20220907

A131 Notification of reasons for refusal

Free format text: JAPANESE INTERMEDIATE CODE: A131

Effective date: 20231128

A521 Request for written amendment filed

Free format text: JAPANESE INTERMEDIATE CODE: A523

Effective date: 20231212

TRDD Decision of grant or rejection written
A01 Written decision to grant a patent or to grant a registration (utility model)

Free format text: JAPANESE INTERMEDIATE CODE: A01

Effective date: 20240305

A61 First payment of annual fees (during grant procedure)

Free format text: JAPANESE INTERMEDIATE CODE: A61

Effective date: 20240318

R150 Certificate of patent or registration of utility model

Ref document number: 7468619

Country of ref document: JP

Free format text: JAPANESE INTERMEDIATE CODE: R150