Copertina

Automated co-design of high-performance thermodynamic cycles via graph-based hierarchical reinforcement learning

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Li, Wenqing, Feng, Xu, Jiang, Peixue, Zhu, Yinhai
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Machine Learning
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Automated co-design of high-performance thermodynamic cycles via graph-based hierarchical reinforcement learning
di: Li, Wenqing
Pubblicazione: (2026)

Flowsheet synthesis through hierarchical reinforcement learning and graph neural networks
di: Stops, Laura, et al.
Pubblicazione: (2022)

Development of low-dissipative projection method framework integrating various high-order time integration schemes using OpenFOAM
di: Guo, Hao, et al.
Pubblicazione: (2023)

Risk-anticipatory autonomous driving strategies considering vehicles' weights, based on hierarchical deep reinforcement learning
di: Chen, Di, et al.
Pubblicazione: (2023)

A novel multi-agent dynamic portfolio optimization learning system based on hierarchical deep reinforcement learning
di: Sun, Ruoyu, et al.
Pubblicazione: (2025)

Surgical Gym: A high-performance GPU-based platform for reinforcement learning with surgical robots
di: Schmidgall, Samuel, et al.
Pubblicazione: (2023)

Emergent temporal abstractions in autoregressive models enable hierarchical reinforcement learning
di: Kobayashi, Seijin, et al.
Pubblicazione: (2025)

Rulebook: bringing co-routines to reinforcement learning environments
di: Fioravanti, Massimo, et al.
Pubblicazione: (2025)

A hierarchical decomposition for explaining ML performance discrepancies
di: Feng, Jean, et al.
Pubblicazione: (2024)

Order-lifted data inversion/retrieval method of neighbor cells to implement general high-order schemes in unstructured-mesh-based finite-volume solution framework
di: Guo, Hao, et al.
Pubblicazione: (2024)

Applications of deep reinforcement learning to urban transit network design
di: Holliday, Andrew
Pubblicazione: (2025)

Causal prompting model-based offline reinforcement learning
di: Yu, Xuehui, et al.
Pubblicazione: (2024)

Agentic reinforcement learning empowers next-generation chemical language models for molecular design and synthesis
di: Li, Hao, et al.
Pubblicazione: (2026)

Physics-Embedded Gaussian Process for Traffic State Estimation
di: Chen, Yanlin, et al.
Pubblicazione: (2025)

An efficient and low-divergence method for generating inhomogeneous and anisotropic turbulence with arbitrary spectra
di: Guo, Hao, et al.
Pubblicazione: (2023)

Explainable reinforcement learning from human feedback to improve alignment
di: Liu, Shicheng, et al.
Pubblicazione: (2025)

HDGL: A hierarchical dynamic graph representation learning model for brain disorder classification
di: Jalali, Parnian, et al.
Pubblicazione: (2023)

Rank-based loss for learning hierarchical representations
di: Nolasco, Ines, et al.
Pubblicazione: (2021)

Improving out-of-distribution generalization in graphs via hierarchical semantic environments
di: Piao, Yinhua, et al.
Pubblicazione: (2024)

CleanSurvival: Automated data preprocessing for time-to-event models using reinforcement learning
di: Koka, Yousef, et al.
Pubblicazione: (2025)

Leveraging LLMs for reward function design in reinforcement learning control tasks
di: Cardenoso, Franklin, et al.
Pubblicazione: (2025)

Attention on flow control: transformer-based reinforcement learning for lift regulation in highly disturbed flows
di: Liu, Zhecheng, et al.
Pubblicazione: (2025)

Ergodicity in reinforcement learning
di: Baumann, Dominik, et al.
Pubblicazione: (2026)

Deep progressive reinforcement learning-based flexible resource scheduling framework for IRS and UAV-assisted MEC system
di: Dong, Li, et al.
Pubblicazione: (2024)

A first realization of reinforcement learning-based closed-loop EEG-TMS
di: Humaidan, Dania, et al.
Pubblicazione: (2026)

Self-supervised local learning rules learn the hidden hierarchical structure of high-dimensional data
di: Delrocq, Ariane, et al.
Pubblicazione: (2026)

Transfer learning strategies for accelerating reinforcement-learning-based flow control
di: Salehi, Saeed
Pubblicazione: (2025)

Optimal scaling laws in learning hierarchical multi-index models
di: Defilippis, Leonardo, et al.
Pubblicazione: (2026)

Model predictive control-based value estimation for efficient reinforcement learning
di: Wu, Qizhen, et al.
Pubblicazione: (2023)

Deep reinforcement learning for irrigation scheduling using high-dimensional sensor feedback
di: Saikai, Yuji, et al.
Pubblicazione: (2023)

Curriculum reinforcement learning with measurable task representation learning
di: Wen, Yongyan, et al.
Pubblicazione: (2026)

Deep learning for the design of non-Hermitian topolectrical circuits
di: Chen, Xi, et al.
Pubblicazione: (2024)

Automated Heterogeneous Network learning with Non-Recursive Message Passing
di: Li, Zhaoqing, et al.
Pubblicazione: (2025)

OThink-MR1: Stimulating multimodal generalized reasoning capabilities via dynamic reinforcement learning
di: Liu, Zhiyuan, et al.
Pubblicazione: (2025)

Variational Autoencoders for exteroceptive perception in reinforcement learning-based collision avoidance
di: Larsen, Thomas Nakken, et al.
Pubblicazione: (2024)

Optimistic Q-learning for average reward and episodic reinforcement learning
di: Agrawal, Priyank, et al.
Pubblicazione: (2024)

Economic span selection of bridge based on deep reinforcement learning
di: Zhang, Leye, et al.
Pubblicazione: (2024)

Towards Heisenberg limit without critical slowing down via quantum reinforcement learning
di: Xu, Hang, et al.
Pubblicazione: (2025)

Boosting long-term forecasting performance for continuous-time dynamic graph networks via data augmentation
di: Tian, Yuxing, et al.
Pubblicazione: (2023)

Numerical solutions of fixed points in two-dimensional Kuramoto-Sivashinsky equation expedited by reinforcement learning
di: Jiang, Juncheng, et al.
Pubblicazione: (2024)