Title: Deep Reinforcement Learning For Wireless Scheduling with Multiclass Services. Abstract: In this paper, we investigate the problem of scheduling and resource allocation over a time varying set of clients with heterogeneous demands. This problem appears when service providers need to serve traffic generated by users with different classes of requirements. We thus have to allocate bandwidth resources over time to efficiently satisfy these demands within a limited time horizon. This is a highly intricate problem and solutions may involve tools stemming from diverse fields like combinatorics and optimization. Recent work has successfully proposed Deep Reinforcement Learning (DRL) solutions, although not yet for heterogeneous user traffic. We propose a deep deterministic policy gradient algorithm combining state of the art techniques, namely Distributional RL and Deep Sets, to train a model for heterogeneous traffic scheduling. We test on diverse number scenarios with different time dependence dynamics, users’ requirements, and resources available, demonstrating consistent results. We evaluate the algorithm on a wireless communication setting and show significant gains against state-of-the-art conventional algorithms from combinatorics and optimization (e.g. Knapsack, Integer Linear Programming, Frank-Wolfe).