Integrating Renewable Energy in Agriculture: A Deep Reinforcement
  Learning-based Approach
A. Wahid I faiud K. Mason
This article investigates the use of Deep Q-Networks (DQNs) to optimize
decision-making for photovoltaic (PV) systems installations in the agriculture
sector. The study develops a DQN framework to assist agricultural investors in
making informed decisions considering factors such as installation budget,
government incentives, energy requirements, system cost, and long-term
benefits. By implementing a reward mechanism, the DQN learns to make
data-driven decisions on PV integration. The analysis provides a comprehensive
understanding of how DQNs can support investors in making decisions about PV
installations in agriculture. This research has significant implications for
promoting sustainable and efficient farming practices while also paving the way
for future advancements in this field. By leveraging DQNs, agricultural
investors can make optimized decisions that improve energy efficiency, reduce
environmental impact, and enhance profitability. This study contributes to the
advancement of PV integration in agriculture and encourages further innovation
in this promising area.