Abstract: A multi-user scheduling method based on reinforcement learning for 5G IoT, including: calculating the achievable rate of each user in a set according to a communication scenario model; generating an initial scheduled users set according to the achievable rate of each user; according to the achievable rate of each user and the number of times each user is scheduled, evaluating the estimated value of each user's action value under the current scheduling period by using the Q-learning method; determining the upper bound value of the confidence interval of each user's action value; determining the set of scheduled users under the current scheduling period according to the upper bound of the confidence interval of each user's action value; according to the set of scheduled users under the current scheduling period, recalculating the actual achievable rate value of each selected user under the current scheduling period.