Results 91 to 100 of about 88,537 (171)

AIMATDESIGN: knowledge-augmented reinforcement learning for inverse materials design under data scarcity

npj Computational Materials
With the growing demand for novel materials, machine learning-driven inverse design methods face significant challenges in reconciling the high-dimensional materials composition space with limited experimental data.
Yeyong Yu, Xilei Bian, Jie Xiong, Xing Wu, Quan Qian   +4 more
doaj   +1 more source

Advancements in Deep Reinforcement Learning and Inverse Reinforcement Learning for Robotic Manipulation: Toward Trustworthy, Interpretable, and Explainable Artificial Intelligence

IEEE Access
This article presents a literature review of the past five years of studies using Deep Reinforcement Learning (DRL) and Inverse Reinforcement Learning (IRL) in robotic manipulation tasks.
Recep Ozalp, Aysegul Ucar, Cuneyt Guzelis   +2 more
doaj   +1 more source

Recursive Deep Inverse Reinforcement Learning

Inferring an adversary's goals from exhibited behavior is crucial for counterplanning and non-cooperative multi-agent systems in domains like cybersecurity, military, and strategy games. Deep Inverse Reinforcement Learning (IRL) methods based on maximum entropy principles show promise in recovering adversaries' goals but are typically offline, require ...
Ghanem, Paul, Howell, Owen, Potter, Michael, Closas, Pau, Ramezani, Alireza, Erdogmus, Deniz, Imbiriba, Tales   +6 more
openaire   +2 more sources

Estimation of Route-Choice Behavior Along LRT Lines Using Inverse Reinforcement Learning

Inventions
As the decline of public transportation in rural areas becomes a growing concern, initiatives to introduce attractive next-generation transportation systems to promote public transportation usage are being considered across various regions.
Tomohiro Okubo, Akihiro Kobayashi, Daisuke Kamisaka, Akinori Morimoto   +3 more
doaj   +1 more source

The Unreasonable Effectiveness of Inverse Reinforcement Learning in Advancing Cancer Research. [PDF]

Proc AAAI Conf Artif Intell, 2020
Kalantari J, Nelson H, Chia N.
europepmc   +1 more source

Predicting Goal-directed Human Attention Using Inverse Reinforcement Learning. [PDF]

Proc IEEE Comput Soc Conf Comput Vis Pattern Recognit, 2020
Yang Z, Huang L, Chen Y, Wei Z, Ahn S, Zelinsky G, Samaras D, Hoai M.   +7 more
europepmc   +1 more source

Inverse Design Using Goal-Conditioned Reinforcement Learning for Organic Semiconductor Materials from Benzene and Thiophene-based Polycyclic Aromatic Compounds

npj Computational Materials
We present a machine learning approach for the inverse design of organic semiconductor materials from benzene and thiophene-based polycyclic aromatic compounds (PACs).
Tri M. Nguyen, Thanh N. Truong
doaj   +1 more source

Sample-efficient inverse design of freeform nanophotonic devices with physics-informed reinforcement learning

Nanophotonics
Finding an optimal device structure in the vast combinatorial design space of freeform nanophotonic design has been an enormous challenge. In this study, we propose physics-informed reinforcement learning (PIRL) that combines the adjoint-based method ...
Park Chaejin, Kim Sanmun, Jung Anthony W., Park Juho, Seo Dongjin, Kim Yongha, Park Chanhyung, Park Chan Y., Jang Min Seok   +8 more
doaj   +1 more source

Goal-oriented autonomous decision-making for social robots via collaborative interactive inverse reinforcement learning approach. [PDF]

Sci Rep
Luo M, Li H, Luo W, Li H, Li J.
europepmc   +1 more source

Eliciting patient preferences and predicting behaviour using Inverse Reinforcement Learning for telehealth use in outpatient clinics. [PDF]

Front Digit Health
Snoswell AJ, Snoswell CL, Ye N.
europepmc   +1 more source