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In this work a general framework is proposed to support the development ofsoftware systems that are able to adapt their behaviour according to theoperating environment changes. The proposed approach, named REPTILE, works in acomplete proactive manner and relies on Deep Reinforcement Learning-basedagents to react to events, referred as novelties, that can affect the expectedbehaviour of the system. In our framework, two types of novelties are takeninto account: those related to the context/environment and those related to thephysical architecture itself. The framework, predicting those novelties beforetheir occurrence, extracts time-changing models of the environment and uses asuitable Markov Decision Process to deal with the real-time setting. Moreover,the architecture of our RL agent evolves based on the possible actions that canbe taken.

REPTILE: A Proactive Real-Time Deep Reinforcement Learning Self-adaptive Framework