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Found 3 papers, 2 papers with code
Robust Model-based Reinforcement Learning for Autonomous Greenhouse Control
In this paper, we present a model-based robust RL framework for autonomous greenhouse control to meet the sample efficiency and safety challenges.
IGrow: A Smart Agriculture Solution to Autonomous Greenhouse Control
However, the optimal control of autonomous greenhouses is challenging, requiring decision-making based on high-dimensional sensory data, and the scaling of production is limited by the scarcity of labor capable of handling this task.
Sample Efficient Reinforcement Learning via Model-Ensemble Exploration and Exploitation
Model-based deep reinforcement learning has achieved success in various domains that require high sample efficiencies, such as Go and robotics.
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