Intelligence without representation
Artificial Intelligence
Technical Note: \cal Q-Learning
Machine Learning
Reinforcement learning of non-Markov decision processes
Artificial Intelligence - Special volume on computational research on interaction and agency, part 2
Automatic control systems (7th ed.)
Automatic control systems (7th ed.)
Using Reinforcement Learning for Similarity Assessment in Case-Based Systems
IEEE Intelligent Systems
Adaptive stock trading with dynamic asset allocation using reinforcement learning
Information Sciences: an International Journal
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Optimal control of greenhouse climate is one of the key techniques in digital agriculture. Greenhouse climate, a nonlinear and uncertain system, consists of several major environmental factors such as temperature, humidity, light intensity, and CO2 concentration. Due to the complex coupled correlations, it is a challenge to achieve coordination control of greenhouse environmental factors. This paper proposes a model-free coordination control approach for greenhouse environmental factors based on Q-learning. Coordination control policy is found through systematic interaction with the dynamic environment to achieve optimal control for greenhouse climate with the control cost constraints. In order to decrease systematic trial-and-error risk and reduce the computational complexity in Q-learning algorithm, case-based reasoning (CBR) is seamlessly incorporated into the Q-learning process. The experimental results demonstrate that this approach is practical, highly effective and efficient.