Results 61 to 70 of about 197,676 (281)

Optimizing Reinforcement Learning Using a Generative Action-Translator Transformer

Algorithms
In recent years, with the rapid advancements in Natural Language Processing (NLP) technologies, large models have become widespread. Traditional reinforcement learning algorithms have also started experimenting with language models to optimize training ...
Jiaming Li, Ning Xie, Tingting Zhao
doaj   +1 more source

Pretraining in Deep Reinforcement Learning: A Survey [PDF]

, 2022
Zhihui Xie, Zichuan Lin, Junyou Li, Shuai Li, Deheng Ye   +4 more
openalex   +1 more source

Lived and Care Experiences of Chronic Musculoskeletal Shoulder Pain in Australian Adults: A Qualitative Study

Arthritis Care &Research, EarlyView.
Objective Australian evidence on lived and care experiences of chronic musculoskeletal shoulder pain (CMSP), irrespective of disorder classification or disease, is limited. However, such evidence is important for person‐centered care and informing local service pathways and care guidelines or standards.
Sonia Ranelli, Joanne E. Jordan, Ilana N. Ackerman, Alison Thorpe, Jennifer G. Persaud, Linda J. Woodhouse, Jason Chua, Ben Horgan, Andrew M. Briggs   +8 more
wiley   +1 more source

Offline Reinforcement Learning with Causal Structured World Models [PDF]

, 2022
Zhengmao Zhu, Xionghui Chen, Hong-Long Tian, Kun Zhang, Yang Yu   +4 more
openalex   +1 more source

A Qualitative Analysis of Patient Perspectives and Preferences in Lupus Management to Guide Lupus Guidelines Development

Arthritis Care &Research, EarlyView.
Objective A patient‐centered approach for chronic disease management, including systemic lupus erythematosus (SLE), aligns treatment with patients’ values and preferences, leading to improved outcomes. This paper summarizes how patient experiences, perspectives, and priorities informed the American College of Rheumatology (ACR) 2024 Lupus Nephritis (LN)
Shivani Garg, Izzy Hartel, Lisa R. Sammaritano, Anca Askanase, Bonnie L. Bermas, Maria Dall'Era, Alí Duarte‐García, Victoria P. Werth, Brad Rovin, Reem A. Mustafa, Amy S. Turner, Bené Williams, Brian L. Ung, Hiya Bhavsar, Monique C. Gore‐Massy, Nahirannette Pulido, Natacha Guerrero, Natalie M. Smith, Wambui Machua, Linda T. Hiraki, Mary Beth Son   +20 more
wiley   +1 more source

Deep Forest Reinforcement Learning for Preventive Strategy Considering Automatic Generation Control in Large-Scale Interconnected Power Systems

Applied Sciences, 2018
To reduce occurrences of emergency situations in large-scale interconnected power systems with large continuous disturbances, a preventive strategy for the automatic generation control (AGC) of power systems is proposed.
Linfei Yin, Lulin Zhao, Tao Yu, Xiaoshun Zhang   +3 more
doaj   +1 more source

An Investigation into Pre-Training Object-Centric Representations for Reinforcement Learning [PDF]

, 2023
Jaesik Yoon, Yifu Wu, Heechul Bae, Sungjin Ahn   +3 more
openalex   +1 more source

Evaluation of a novel EHR sidecar application to display RA clinical outcomes during clinic visits: results of a stepped‐wedge cluster randomized pragmatic trial

Arthritis Care &Research, Accepted Article.
Objective We developed a novel EHR sidecar application to visualize key rheumatoid arthritis (RA) outcomes, including disease activity, physical function, and pain, via a patient‐facing graphical interface designed for use during outpatient visits (“RA PRO dashboard”).
Gabriela Schmajuk, Jing Li, Cathy Nasrallah, Cherish Wilson, Alicia Hamblin, Christine Hariz, Cammie Young, Stephen Shiboski, Emma Kersey, Mary Nakamura, Judith F. Ashouri, Mehrdad Matloubian, Andrew J. Gross, Jennifer Barton, Beth Berrean, Jonathan Prugh, Jinoos Yazdany   +16 more
wiley   +1 more source

Using Inverse Reinforcement Learning with Real Trajectories to Get More Trustworthy Pedestrian Simulations

Mathematics, 2020
Reinforcement learning is one of the most promising machine learning techniques to get intelligent behaviors for embodied agents in simulations. The output of the classic Temporal Difference family of Reinforcement Learning algorithms adopts the form of ...
Francisco Martinez-Gil, Miguel Lozano, Ignacio García-Fernández, Pau Romero, Dolors Serra, Rafael Sebastián   +5 more
doaj   +1 more source

Constrained Reinforcement Learning for Robotics via Scenario-Based Programming [PDF]

, 2022
Davide Corsi, Raz Yerushalmi, Guy Amir, Alessandro Farinelli, David Harel, Guy Katz   +5 more
openalex   +1 more source