  A reinforcement-learning-based non-uniform compressed sensing (NCS) framework
for time-varying signals is introduced. The proposed scheme, referred to as
RL-NCS, aims to boost the performance of signal recovery through an optimal and
adaptive distribution of sensing energy among two groups of coefficients of the
signal, referred to as the region of interest (ROI) coefficients and non-ROI
coefficients. The coefficients in ROI usually have greater importance and need
to be reconstructed with higher accuracy compared to non-ROI coefficients. In
order to accomplish this task, the ROI is predicted at each time step using two
specific approaches. One of these approaches incorporates a long short-term
memory (LSTM) network for the prediction. The other approach employs the
previous ROI information for predicting the next step ROI. Using the
exploration-exploitation technique, a Q-network learns to choose the best
approach for designing the measurement matrix. Furthermore, a joint loss
function is introduced for the efficient training of the Q-network as well as
the LSTM network. The result indicates a significant performance gain for our
proposed method, even for rapidly varying signals and a reduced number of
measurements.
