10 citations · 20 across the 15 of their papers we have counts for
15 papers
A Hybrid Reinforcement and Self-Supervised Learning Aided Benders Decomposition Algorithm
Bernard T. Agyeman, Zhe Li, Ilias Mitrai +1
We propose a hybrid reinforcement and self-supervised learning framework for accelerating generalized Benders decomposition (GBD). In this framework, a graph based reinforcement le…
Feasibility-Aware Imitation Learning for Benders Decomposition
Bernard T. Agyeman, Zhe Li, Ilias Mitrai +1
Mixed-integer optimization problems arise in a wide range of control applications. Benders decomposition is a widely used algorithm for solving such problems by decomposing them in…
Graph-Based Imitation and Reinforcement Learning for Efficient Benders Decomposition
Bernard T. Agyeman, Zhe Li, Ilias Mitrai +1
This work introduces an end-to-end graph-based agent for accelerating the computational efficiency of Benders Decomposition. The agent's policy is parameterized by a graph neural n…
Learning to control inexact Benders decomposition via reinforcement learning
Zhe Li, Bernard T. Agyeman, Ilias Mitrai +1
Benders decomposition (BD), along with its generalized version (GBD), is a widely used algorithm for solving large-scale mixed-integer optimization problems that arise in the opera…
ReLU Surrogates in Mixed-Integer MPC for Irrigation Scheduling
Bernard T. Agyeman, Jinfeng Liu, Sirish L. Shah
Efficient water management in agriculture is important for mitigating the growing freshwater scarcity crisis. Mixed-integer Model Predictive Control (MPC) has emerged as an effecti…
A semi-centralized multi-agent RL framework for efficient irrigation scheduling
Bernard T. Agyeman, Benjamin Decard-Nelson, Jinfeng Liu +1
This paper proposes a Semi-Centralized Multi-Agent Reinforcement Learning (SCMARL) approach for irrigation scheduling in spatially variable agricultural fields, where management zo…
Performance triggered adaptive model reduction for soil moisture estimation in precision irrigation
Sarupa Debnath, Bernard T. Agyeman, Soumya R. Sahoo +2
Accurate soil moisture information is crucial for developing precise irrigation control strategies to enhance water use efficiency. Soil moisture estimation based on limited soil m…
Optimizing the switching operation in monoclonal antibody production: Economic MPC and reinforcement learning
Sandra A. Obiri, Song Bo, Bernard T. Agyeman +2
Monoclonal antibodies (mAbs) have emerged as indispensable assets in medicine, and are currently at the forefront of biopharmaceutical product development. However, the growing mar…
Integrating machine learning paradigms and mixed-integer model predictive control for irrigation scheduling
Bernard T. Agyeman, Mohamed Naouri, Willemijn Appels +2
The agricultural sector currently faces significant challenges in water resource conservation and crop yield optimization, primarily due to concerns over freshwater scarcity. Tradi…
Control invariant set enhanced safe reinforcement learning: improved sampling efficiency, guaranteed stability and robustness
Song Bo, Bernard T. Agyeman, Xunyuan Yin +1
Reinforcement learning (RL) is an area of significant research interest, and safe RL in particular is attracting attention due to its ability to handle safety-driven constraints th…
Sensitivity-Informed Parameter Selection for Improved Soil Moisture Estimation from Remote Sensing Data
Bernard T. Agyeman, Erfan Orouskhani, Mohamed Naouri +4
Improving the accuracy of soil moisture estimation is required for advancing irrigation scheduling and water conservation efforts. Central to this task are soil hydraulic parameter…
Impact of sensor placement in soil water estimation: A real-case study
Erfan Orouskhani, Soumya R. Sahoo, Bernard T. Agyeman +2
One of the essential elements in implementing a closed-loop irrigation system is soil moisture estimation based on a limited number of available sensors. One associated problem is…
Knowledge-based optimal irrigation scheduling of agro-hydrological systems
Soumya R. Sahoo, Bernard T. Agyeman, Sarupa Debnath +1
The typical agricultural irrigation scheduler provides information on how much to irrigate and when to irrigate. The accurate and effective scheduler decision for a large agricultu…
LSTM-based model predictive control with discrete inputs for irrigation scheduling
Bernard T. Agyeman, Soumya R. Sahoo, Jinfeng Liu +1
The development of well-devised irrigation scheduling methods is desirable from the perspectives of plant quality and water conservation. In this article, a model predictive contro…
Soil moisture map construction using microwave remote sensors and sequential data assimilation
Bernard T. Agyeman, Song Bo, Soumya R. Sahoo +3
Microwave remote sensors mounted on center pivot irrigation systems provide a feasible approach to obtain soil moisture information, in the form of water content maps, for the impl…