Asthma attack prediction is a highly challenging problem because of the dynamic and multi-factor nature of its etiology. Disease severity level, physiological measurements, patient behaviors and characteristics, environmental triggers, and Personal Risk Scores (RS) are among the strong predictors of an asthma attack. In this paper, we propose a Deep Reinforcement Leaning framework to predict asthma attacks using historical data linking the severity level of the disease and the personalized risk scores of triggers. Deep Q-learning based prediction framework can model future reward explicitly. Besides, the risk scores of triggers which are calculated using Additive Interaction Analysis of Exposures technique helps increase the prediction performance. The main purpose of this study is to investigate the ability of using Q-learning method to create a prediction model that would help asthmatic individuals to take evasive action when the probability of an attack was at their personal threshold levels.