Robust Model-based Reinforcement Learning for Autonomous Greenhouse
  Control
  Due to the high efficiency and less weather dependency, autonomous
greenhouses provide an ideal solution to meet the increasing demand for fresh
food. However, managers are faced with some challenges in finding appropriate
control strategies for crop growth, since the decision space of the greenhouse
control problem is an astronomical number. Therefore, an intelligent
closed-loop control framework is highly desired to generate an automatic
control policy. As a powerful tool for optimal control, reinforcement learning
(RL) algorithms can surpass human beings' decision-making and can also be
seamlessly integrated into the closed-loop control framework. However, in
complex real-world scenarios such as agricultural automation control, where the
interaction with the environment is time-consuming and expensive, the
application of RL algorithms encounters two main challenges, i.e., sample
efficiency and safety. Although model-based RL methods can greatly mitigate the
efficiency problem of greenhouse control, the safety problem has not got too
much attention. In this paper, we present a model-based robust RL framework for
autonomous greenhouse control to meet the sample efficiency and safety
challenges. Specifically, our framework introduces an ensemble of environment
models to work as a simulator and assist in policy optimization, thereby
addressing the low sample efficiency problem. As for the safety concern, we
propose a sample dropout module to focus more on worst-case samples, which can
help improve the adaptability of the greenhouse planting policy in extreme
cases. Experimental results demonstrate that our approach can learn a more
effective greenhouse planting policy with better robustness than existing
methods.
