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Evaluating the readability of a text can significantly facilitate the preciseexpression of information in written form. The formulation of text readabilityassessment involves the identification of meaningful properties of the textregardless of its length. Sophisticated features and models are used toevaluate the comprehensibility of texts accurately. Despite this, the problemof assessing texts' readability efficiently remains relatively untouched. Theefficiency of state-of-the-art text readability assessment models can befurther improved using deep reinforcement learning models. Using a hardattention-based active inference technique, the proposed approach makesefficient use of input text and computational resources. Through the use ofsemi-supervised signals, the reinforcement learning model uses the minimumamount of text in order to determine text's readability. A comparison of themodel on Weebit and Cambridge Exams with state-of-the-art models, such as theBERT text readability model, shows that it is capable of achievingstate-of-the-art accuracy with a significantly smaller amount of input textthan other models.

Text as Environment: A Deep Reinforcement Learning Text Readability Assessment Model