Method for determining a recommendation value of a control parameter of a fluid infusion device, the method being implemented by a control device and comprising the steps of: retrieving user data, each data of the user data having a timestamp and the user data being related to a unique user, the user data comprising at least: a plurality of amounts of a drug infused to the unique user; a plurality of physiological values of the unique user; a plurality of estimated values; and wherein a plurality of amounts of a drug infused to the unique user, a physiological value of the unique user of the plurality of physiological values of the unique user and an estimated value of the plurality of estimated values represent a state of the unique user, feeding a deep reinforcement learning network, such as the step of feeding a deep reinforcement learning network consists of giving a state of the unique user as an input to a deep reinforcement learning network, said deep reinforcement learning network comprising at least two layers; outputting a deep reinforcement learning network result, such as the step of outputting the deep reinforcement learning network result consists of outputting a deep reinforcement learning network result for at least a penultimate layer of the at least two layers; feeding uncertainty certificates, such as the step of feeding the uncertainty certificates consists of giving a result of the penultimate layer of the at least two layers as an input to said uncertainty certificates; outputting an uncertainty certificate result, such as the step of outputting the uncertainty certificate result consists of outputting an uncertainty certificate result; comparing the uncertainty certificate result, such as the step of comparing the uncertainty result consists of comparing the uncertainty result to an uncertainty threshold value, wherein the uncertainty threshold value represents a value under which the deep reinforcement learning network output is considered certain, otherwise, the deep reinforcement learning network output is considered uncertain; and if the deep reinforcement learning network is considered certain, determining the recommendation value of a control parameter of the fluid infusion device based on a state of the unique user using the deep reinforcement learning network; else determining the recommendation value of a control parameter of the fluid infusion device based on a state of the unique user using another control algorithm. a plurality of amounts of a drug infused to the unique user; a plurality of physiological values of the unique user; a plurality of estimated values; and wherein a plurality of amounts of a drug infused to the unique user, a physiological value of the unique user of the plurality of physiological values of the unique user and an estimated value of the plurality of estimated values represent a state of the unique user, feeding a deep reinforcement learning network, such as the step of feeding a deep reinforcement learning network consists of giving a state of the unique user as an input to a deep reinforcement learning network, said deep reinforcement learning network comprising at least two layers; outputting a deep reinforcement learning network result, such as the step of outputting the deep reinforcement learning network result consists of outputting a deep reinforcement learning network result for at least a penultimate layer of the at least two layers; feeding uncertainty certificates, such as the step of feeding the uncertainty certificates consists of giving a result of the penultimate layer of the at least two layers as an input to said uncertainty certificates; outputting an uncertainty certificate result, such as the step of outputting the uncertainty certificate result consists of outputting an uncertainty certificate result; comparing the uncertainty certificate result, such as the step of comparing the uncertainty result consists of comparing the uncertainty result to an uncertainty threshold value, wherein the uncertainty threshold value represents a value under which the deep reinforcement learning network output is considered certain, otherwise, the deep reinforcement learning network output is considered uncertain; and if the deep reinforcement learning network is considered certain, determining the recommendation value of a control parameter of the fluid infusion device based on a state of the unique user using the deep reinforcement learning network; else determining the recommendation value of a control parameter of the fluid infusion device based on a state of the unique user using another control algorithm.