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Coordinated beamforming is very efficient at managing interference in ultra dense network. However, the optimal strategy remains as a challenge task to obtain due to the coupled nature among densely and autonomously deployed cells. In this paper, the deep reinforcement learning is investigated for predicting coordinated beamforming strategy. Formulated as a sum-rate maximization problem, the optimal solution turns out as a balanced combination of selfish and altruistic beamforming. As the balancing coefficients depend on the beamforming vectors of all the cells, iterations are inevitable to get the final solution. To address this problem and improve efficiency, deep reinforcement learning (DL) is proposed to predict the balancing coefficients. Specifically, the agent, on behalf of a base station-user pair, will rely on Deep Q-network to learn the highly complex mapping between the balancing coefficients and …