  A cognitive beamforming algorithm for colocated MIMO radars, based on
Reinforcement Learning (RL) framework, is proposed. We analyse an RL-based
optimization protocol that allows the MIMO radar, i.e. the \textit{agent}, to
iteratively sense the unknown environment, i.e. the radar scene involving an
unknown number of targets at unknown angular positions, and consequently, to
synthesize a set of transmitted waveforms whose related beam patter is tailored
on the acquired knowledge. The performance of the proposed RL-based beamforming
algorithm is assessed through numerical simulations in terms of Probability of
Detection ($P_D$).
