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.)
Optimized fuzzy control of a greenhouse
Fuzzy Sets and Systems - Featured Issue: Selected papers from ACIDCA 2000
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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Greenhouse climate is a multiple coupled variable, nonlinear and uncertain system. It consists of several major environmental factors, such as temperature, humidity, light intensity, and CO2 concentration. In this work, we propose a constraint optimal control approach for greenhouse climate. Instead of modeling greenhouse climate, Q-learning is introduced to search for optimal control strategy through trial-and-error interaction with the dynamic environment. The coupled relations among greenhouse environmental factors are handled by coordinating the different control actions. The reinforcement signal is designed with consideration of the control action costs. To decrease systematic trial-and-error risk and reduce the computational complexity in Q-learning algorithm Case Based Reasoning (CBR) is seamlessly incorporated into Q-learning process of the optimal control. The experimental results show this approach is practical, highly effective and efficient.