10 citations · 15 across the 11 of their papers we have counts for
7 papers · 1 filter
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…
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…
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…