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In the present article, a reinforcement learning (RL)-based  adaptive  algorithm to optimize the transmit beampattern for a colocated massive multiple-input multiple-output (MIMO) radar is presented. Under the massive MIMO regime, a robust Wald-type detector, able to guarantee certain detection performances under a wide range of practical disturbance models, has been recently proposed. Furthermore, an RL/cognitive methodology has been exploited to improve the detection performance by learning and interacting with the surrounding unknown environment. Building upon previous findings, we develop here a fully adaptive and  data-driven  scheme for the selection of the hyperparameters involved in the RL algorithm. Such an adaptive selection makes the Wald-RL-based detector independent of any  ad hoc , and potentially suboptimal, manual tuning of the hyperparameters. Simulation results show the effectiveness of the proposed scheme in harsh scenarios with strong clutter and low SNR values.

Enhancement of a State-of-the-Art RL-Based Detection Algorithm for Massive MIMO Radars