scholarly journals Scaling data-driven robotics with reward sketching and batch reinforcement learning

2020 ◽  
Author(s):  
Serkan Cabi ◽  
Sergio Gómez Colmenarejo ◽  
Alexander Novikov ◽  
Ksenia Konyushova ◽  
Scott Reed ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Data Driven ◽  
Batch Reinforcement Learning

scholarly journals Data-driven dynamic multi-objective optimal control: A Hamiltonian-inequality driven satisficing reinforcement learning approach

2020 ◽  
Vol 53 (2) ◽  
pp. 8070-8075
Author(s):  
Majid Mazouchi ◽  
Yongliang Yang ◽  
Hamidreza Modares
Keyword(s):  
Optimal Control ◽  
Reinforcement Learning ◽  
Data Driven ◽  
Learning Approach ◽  
Multi Objective

scholarly journals Deep Reinforcement Learning With Spatio-Temporal Traffic Forecasting for Data-Driven Base Station Sleep Control

2021 ◽  
pp. 1-14
Author(s):  
Qiong Wu ◽  
Xu Chen ◽  
Zhi Zhou ◽  
Liang Chen ◽  
Junshan Zhang
Keyword(s):  
Reinforcement Learning ◽  
Base Station ◽  
Data Driven ◽  
Traffic Forecasting ◽  
Spatio Temporal ◽  
Sleep Control

scholarly journals Optimising Performance for NB-IoT UE Devices through Data Driven Models

2021 ◽  
Vol 10 (1) ◽  
pp. 21
Author(s):  
Omar Nassef ◽  
Toktam Mahmoodi ◽  
Foivos Michelinakis ◽  
Kashif Mahmood ◽  
Ahmed Elmokashfi
Keyword(s):  
Neural Network ◽  
Reinforcement Learning ◽  
Gradient Descent ◽  
Deep Neural Network ◽  
Narrow Band ◽  
Learning Algorithm ◽  
Base Station ◽  
User Equipment ◽  
Data Driven ◽  
Superior Performance

This paper presents a data driven framework for performance optimisation of Narrow-Band IoT user equipment. The proposed framework is an edge micro-service that suggests one-time configurations to user equipment communicating with a base station. Suggested configurations are delivered from a Configuration Advocate, to improve energy consumption, delay, throughput or a combination of those metrics, depending on the user-end device and the application. Reinforcement learning utilising gradient descent and genetic algorithm is adopted synchronously with machine and deep learning algorithms to predict the environmental states and suggest an optimal configuration. The results highlight the adaptability of the Deep Neural Network in the prediction of intermediary environmental states, additionally the results present superior performance of the genetic reinforcement learning algorithm regarding its performance optimisation.

scholarly journals Effective Treatment Recommendations for Type 2 Diabetes Management Using Reinforcement Learning: Treatment Recommendation Model Development and Validation (Preprint)

2021 ◽  
Author(s):  
Xingzhi Sun ◽  
Yong Mong Bee ◽  
Shao Wei Lam ◽  
Zhuo Liu ◽  
Wei Zhao ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Type 2 Diabetes ◽  
Reinforcement Learning ◽  
Blood Lipids ◽  
Diabetes Complications ◽  
Data Driven ◽  
Lipid Lowering ◽  
Treatment Recommendation

BACKGROUND Type 2 diabetes mellitus (T2DM) and its related complications represent a growing economic burden for many countries and health systems. Diabetes complications can be prevented through better disease control, but there is a large gap between the recommended treatment and the treatment that patients actually receive. The treatment of T2DM can be challenging because of different comprehensive therapeutic targets and individual variability of the patients, leading to the need for precise, personalized treatment. OBJECTIVE The aim of this study was to develop treatment recommendation models for T2DM based on deep reinforcement learning. A retrospective analysis was then performed to evaluate the reliability and effectiveness of the models. METHODS The data used in our study were collected from the Singapore Health Services Diabetes Registry, encompassing 189,520 patients with T2DM, including 6,407,958 outpatient visits from 2013 to 2018. The treatment recommendation model was built based on 80% of the dataset and its effectiveness was evaluated with the remaining 20% of data. Three treatment recommendation models were developed for antiglycemic, antihypertensive, and lipid-lowering treatments by combining a knowledge-driven model and a data-driven model. The knowledge-driven model, based on clinical guidelines and expert experiences, was first applied to select the candidate medications. The data-driven model, based on deep reinforcement learning, was used to rank the candidates according to the expected clinical outcomes. To evaluate the models, short-term outcomes were compared between the model-concordant treatments and the model-nonconcordant treatments with confounder adjustment by stratification, propensity score weighting, and multivariate regression. For long-term outcomes, model-concordant rates were included as independent variables to evaluate if the combined antiglycemic, antihypertensive, and lipid-lowering treatments had a positive impact on reduction of long-term complication occurrence or death at the patient level via multivariate logistic regression. RESULTS The test data consisted of 36,993 patients for evaluating the effectiveness of the three treatment recommendation models. In 43.3% of patient visits, the antiglycemic medications recommended by the model were concordant with the actual prescriptions of the physicians. The concordant rates for antihypertensive medications and lipid-lowering medications were 51.3% and 58.9%, respectively. The evaluation results also showed that model-concordant treatments were associated with better glycemic control (odds ratio [OR] 1.73, 95% CI 1.69-1.76), blood pressure control (OR 1.26, 95% CI, 1.23-1.29), and blood lipids control (OR 1.28, 95% CI 1.22-1.35). We also found that patients with more model-concordant treatments were associated with a lower risk of diabetes complications (including 3 macrovascular and 2 microvascular complications) and death, suggesting that the models have the potential of achieving better outcomes in the long term. CONCLUSIONS Comprehensive management by combining knowledge-driven and data-driven models has good potential to help physicians improve the clinical outcomes of patients with T2DM; achieving good control on blood glucose, blood pressure, and blood lipids; and reducing the risk of diabetes complications in the long term.

scholarly journals Data-Driven Economic NMPC Using Reinforcement Learning

2020 ◽  
Vol 65 (2) ◽  
pp. 636-648 ◽  
Author(s):  
Sebastien Gros ◽  
Mario Zanon
Keyword(s):  
Reinforcement Learning ◽  
Data Driven

A Data-Driven Multi-Agent Autonomous Voltage Control Framework Using Deep Reinforcement Learning

2020 ◽  
Vol 35 (6) ◽  
pp. 4644-4654 ◽  
Author(s):  
Shengyi Wang ◽  
Jiajun Duan ◽  
Di Shi ◽  
Chunlei Xu ◽  
Haifeng Li ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Voltage Control ◽  
Data Driven ◽  
Control Framework ◽  
Multi Agent
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