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Related papers: Machine Learning aided Crop Yield Optimization

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The online optimization of gasoline blending benefits refinery economies. However, the nonlinear blending mechanism, the oil property fluctuations, and the blending model mismatch bring difficulties to the optimization. To solve the above…

Computational Engineering, Finance, and Science · Computer Science 2023-09-07 Muyi Huang , Renchu He , Xin Dai , Xin Peng , Wenli Du , Feng Qian

Addressing a real world sequential decision problem with Reinforcement Learning (RL) usually starts with the use of a simulated environment that mimics real conditions. We present a novel open source RL environment for realistic crop…

Artificial Intelligence · Computer Science 2022-09-28 Romain Gautron , Emilio J. Padrón , Philippe Preux , Julien Bigot , Odalric-Ambrym Maillard , David Emukpere

Reinforcement Learning (RL) has emerged as a powerful paradigm in Artificial Intelligence (AI), enabling agents to learn optimal behaviors through interactions with their environments. Drawing from the foundations of trial and error, RL…

Artificial Intelligence · Computer Science 2025-02-04 Majid Ghasemi , Amir Hossein Moosavi , Dariush Ebrahimi

Increasingly complex, non-linear World-Earth system models are used for describing the dynamics of the biophysical Earth system and the socio-economic and socio-cultural World of human societies and their interactions. Identifying pathways…

Physics and Society · Physics 2020-09-16 Felix M. Strnad , Wolfram Barfuss , Jonathan F. Donges , Jobst Heitzig

Deep Reinforcement Learning (DRL) has recently spread into a range of domains within physics and engineering, with multiple remarkable achievements. Still, much remains to be explored before the capabilities of these methods are well…

Computational Engineering, Finance, and Science · Computer Science 2020-12-22 Jonathan Viquerat , Jean Rabault , Alexander Kuhnle , Hassan Ghraieb , Aurélien Larcher , Elie Hachem

Deep reinforcement learning (RL) algorithms can learn complex policies to optimize agent operation over time. RL algorithms have shown promising results in solving complicated problems in recent years. However, their application on…

Machine Learning · Computer Science 2021-09-29 Hamed Khorasgani , Haiyan Wang , Chetan Gupta , Susumu Serita

Recently, deep reinforcement learning (DRL)-based approach has shown promisein solving complex decision and control problems in power engineering domain.In this paper, we present an in-depth analysis of DRL-based voltage control fromaspects…

Artificial Intelligence · Computer Science 2020-12-25 Xiren Zhou , Siqi Wang , Ruisheng Diao , Desong Bian , Jiahui Duan , Di Shi

Path planning is an important problem with the the applications in many aspects, such as video games, robotics etc. This paper proposes a novel method to address the problem of Deep Reinforcement Learning (DRL) based path planning for a…

Robotics · Computer Science 2024-04-11 Hao Liu , Yi Shen , Shuangjiang Yu , Zijun Gao , Tong Wu

The field development optimization (FDO) problem represents a challenging mixed-integer nonlinear programming (MINLP) problem in which we seek to obtain the number of wells, their type, location, and drilling sequence that maximizes an…

Signal Processing · Electrical Eng. & Systems 2022-07-22 Yusuf Nasir

The popularity of deep reinforcement learning (DRL) methods in economics have been exponentially increased. DRL through a wide range of capabilities from reinforcement learning (RL) and deep learning (DL) for handling sophisticated dynamic…

Statistical Finance · Quantitative Finance 2020-04-06 Amir Mosavi , Pedram Ghamisi , Yaser Faghan , Puhong Duan

Forecasting crop yields is important for food security, in particular to predict where crop production is likely to drop. Climate records and remotely-sensed data have become instrumental sources of data for crop yield forecasting systems.…

Applications · Statistics 2021-04-29 Michele Meroni , François Waldner , Lorenzo Seguini , Hervé Kerdiles , Felix Rembold

The rapid growth of data across fields of science and industry has increased the need to improve the performance of end-to-end data transfers while using the resources more efficiently. In this paper, we present a dynamic, multiparameter…

Distributed, Parallel, and Cluster Computing · Computer Science 2026-03-27 Hasibul Jamil , Jacob Goldverg , Elvis Rodrigues , MD S Q Zulkar Nine , Tevfik Kosar

Crop yield is a highly complex trait determined by multiple factors such as genotype, environment, and their interactions. Accurate yield prediction requires fundamental understanding of the functional relationship between yield and these…

Machine Learning · Computer Science 2019-06-12 Saeed Khaki , Lizhi Wang

The evaluation of the impact of using Machine Learning in the management of softwarized networks is considered in multiple research works. Beyond that, we propose to evaluate the robustness of online learning for optimal network slice…

Networking and Internet Architecture · Computer Science 2021-08-21 Jose Jurandir Alves Esteves , Amina Boubendir , Fabrice Guillemin , Pierre Sens

Deep Reinforcement Learning (DRL) has become a popular method for solving control problems in power systems. Conventional DRL encourages the agent to explore various policies encoded in a neural network (NN) with the goal of maximizing the…

Systems and Control · Electrical Eng. & Systems 2024-10-28 Tong Wu , Anna Scaglione , Daniel Arnold

Distributional reinforcement learning (DRL) is a recent reinforcement learning framework whose success has been supported by various empirical studies. It relies on the key idea of replacing the expected return with the return distribution,…

Machine Learning · Computer Science 2020-01-09 Rahul Singh , Keuntaek Lee , Yongxin Chen

Portfolio optimization is essential for balancing risk and return in financial decision-making. Deep Reinforcement Learning (DRL) has stood out as a cutting-edge tool for portfolio optimization that learns dynamic asset allocation using…

Machine Learning · Computer Science 2025-09-16 Himanshu Choudhary , Arishi Orra , Manoj Thakur

Crop yield prediction typically involves the utilization of either theory-driven process-based crop growth models, which have proven to be difficult to calibrate for local conditions, or data-driven machine learning methods, which are known…

Reinforcement Learning (RL) is increasingly applied to large-scale decision-making problems like logistics, scheduling, and recommender systems, but existing algorithms struggle with the curse of dimensionality in such large discrete action…

Machine Learning · Computer Science 2026-05-12 Heiko Hoppe , Fabian Akkerman , Wouter van Heeswijk , Maximilian Schiffer

Network slicing is born as an emerging business to operators, by allowing them to sell the customized slices to various tenants at different prices. In order to provide better-performing and cost-efficient services, network slicing involves…

Networking and Internet Architecture · Computer Science 2018-11-22 Rongpeng Li , Zhifeng Zhao , Qi Sun , Chi-Lin I , Chenyang Yang , Xianfu Chen , Minjian Zhao , Honggang Zhang