  In this paper, the problem of pilot contamination in a multi-cell massive
multiple input multiple output (M-MIMO) system is addressed using deep
reinforcement learning (DRL). To this end, a pilot assignment strategy is
designed that adapts to the channel variations while maintaining a tolerable
pilot contamination effect. Using the angle of arrival (AoA) information of the
users, a cost function, portraying the reward, is presented, defining the pilot
contamination effects in the system. Numerical results illustrate that the
DRL-based scheme is able to track the changes in the environment, learn the
near-optimal pilot assignment, and achieve a close performance to that of the
optimum pilot assignment performed by exhaustive search, while maintaining a
low computational complexity.
