Browse by Subject Reinforcement learning
Showing results 23 to 39 of 39
| Learning-based resource partitioning in heterogeneous networks with multiple network operators Chung, Byung Chang; Cho, Dong-Ho, IEEE COMMUNICATIONS LETTERS, v.25, no.3, pp.869 - 873, 2021-03 |
| Machine learning: Overview of the recent progresses and implications for the process systems engineering field Lee, Jay Hyung; Shin, Joohyun; Realff, Matthew J., COMPUTERS & CHEMICAL ENGINEERING, v.114, pp.111 - 121, 2018-06 |
| Modular Q-학습 기반 다개체 협력 시스템과 로봇 축구에의 적용 = Modular Q learning-based multi-agent cooperation system and its application to robot soccerlink 박귀홍; Park, Kui-Hong; et al, 한국과학기술원, 1999 |
| Multi-step lookahead Bayesian optimization with active learning using reinforcement learning and its application to data-driven batch-to-batch optimization Byun, Ha Eun; Kim, Bo Eun; Lee, Jay Hyung, COMPUTERS & CHEMICAL ENGINEERING, v.167, 2022-11 |
| Multi-timescale, multi-period decision-making model development by combining reinforcement learning and mathematical programming Shin, Joohyun; Lee, Jay Hyung, COMPUTERS & CHEMICAL ENGINEERING, v.121, pp.556 - 573, 2019-02 |
| OmniDRL: An Energy-Efficient Deep Reinforcement Learning Processor With Dual-Mode Weight Compression and Sparse Weight Transposer Lee, Juhyoung; Kim, Sangyeob; Kim, Sangjin; Jo, Wooyoung; Kim, Ji-Hoon; Han, Donghyeon; Yoo, Hoi-Jun, IEEE JOURNAL OF SOLID-STATE CIRCUITS, v.57, no.4, pp.999 - 1012, 2022-04 |
| Online actor-critic method based on incrementally generated radial basis functions = 점진적으로 생성되는 방사형 기저함수 기반 온라인 액터-크리틱 방법link Lee, Dong-Hyun; 이동현; et al, 한국과학기술원, 2013 |
| Path optimization for marine vehicles in ocean currents using reinforcement learning Yoo, Byunghyun; Kim, Jinwhan, JOURNAL OF MARINE SCIENCE AND TECHNOLOGY, v.21, no.2, pp.334 - 343, 2016-06 |
| Quantum-classical reinforcement learning for decoding noisy classical parity information Park, Daniel K.; Park, Jonghun; Rhee, June-Koo Kevin, QUANTUM MACHINE INTELLIGENCE, v.2, no.1, 2020-06 |
Reinforcement Learning Based Optimal Control of Batch Processes Using Monte-Carlo Deep Deterministic Policy Gradient with Phase Segmentation Yoo, Haeun; Kim, Boeun; Kim, Jong Woo; Lee, Jay Hyung, COMPUTERS & CHEMICAL ENGINEERING, v.144, pp.107133, 2021-01 |
| Scaling simulation-to-real transfer by learning a latent space of robot skills Julian, Ryan C.; Heiden, Eric; He, Zhanpeng; Zhang, Hejia; Schaal, Stefan; Lim, Joseph J.; Sukhatme, Gaurav S.; et al, INTERNATIONAL JOURNAL OF ROBOTICS RESEARCH, v.39, no.10-11, pp.1259 - 1278, 2020-09 |
| Self-organizing fuzzy inference system by Q-learning = Q-학습에 의한 자기 형성 퍼지 추론 시스템link Kim, Min-Soeng; 김민성; et al, 한국과학기술원, 1999 |
| Structural optimization of an arch-structured epoxy/rubber composite vibration isolator using deep Q-value neural network reinforcement learning Hong, Hyunsoo; Jeong, Kwang Il; On, Seung Yoon; Kim, Wonvin; Kim, Seong Su, COMPOSITE STRUCTURES, v.323, 2023-11 |
| The Reinforcement Learning based Local Routing Optimization for Ad-hoc Network Lee, Yongsu; Woo, Jongchan; Yoo, Hoi-Jun, JOURNAL OF SEMICONDUCTOR TECHNOLOGY AND SCIENCE, v.19, no.1, pp.137 - 143, 2019-02 |
| Transferable traffic signal control: Reinforcement learning with graph centric state representation Yoon, Jinwon; Ahn, Kyuree; Park, Jinkyoo; Yeo, Hwasoo, TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES, v.130, 2021-09 |
| 게임과 로봇공학에서의 모델 프리 강화학습 응용에 대한 사례 조사 김세원; 이재길, 데이타베이스연구, v.35, no.2, pp.126 - 138, 2019-08 |
| 인공지능 기반 드론 목표물 추적 시스템의 설계 및 구현 강완주; 구윤표; 방지환; 손경환; Hostallero, David; 윤세은; 여현호; et al, 한국통신학회논문지, v.42, no.12, pp.2391 - 2401, 2017-12 |
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