Abstract: Techniques for using deep reinforcement learning for training a recommendation model for an online service are disclosed herein. In some embodiments, a computer-implemented method comprises training a recommendation model using deep reinforcement learning and a Markov decision process, where the Markov decision process has a state space including state embeddings of a plurality of reference users, an action space including action embeddings of the plurality of reference users, and a reward function. The reward function may be configured to issue a first reward based on current impression interaction data and a second reward based on a measurement of engagement of the reference user with the online service.