An information providing device includes an agent ECU that sets a reward function through the use of history data on a response, from a driver, to an operation proposal for an in-vehicle component, and calculates a probability distribution of performance of each of actions constructing an action space in each of states constructing a state space, through reinforced learning based on the reward function. The agent ECU calculates a dispersion degree of the probability distribution. The agent ECU makes a trial-and-error operation proposal to select a target action from a plurality of candidates and output the target action when the dispersion degree of the probability distribution is equal to or larger than a threshold, and makes a definitive operation proposal to fix and output a target action when the value of the dispersion degree of the probability distribution is smaller than the threshold.