  Accelerated magnetic resonance imaging resorts to either Fourier-domain
subsampling or better reconstruction algorithms to deal with fewer measurements
while still generating medical images of high quality. Determining the optimal
sampling strategy given a fixed reconstruction protocol often has combinatorial
complexity. In this work, we apply double deep Q-learning and REINFORCE
algorithms to learn the sampling strategy for dynamic image reconstruction. We
consider the data in the format of time series, and the reconstruction method
is a pre-trained autoencoder-typed neural network. We present a proof of
concept that reinforcement learning algorithms are effective to discover the
optimal sampling pattern which underlies the pre-trained reconstructor network
(i.e., the dynamics in the environment). The code for replicating experiments
can be found at https://github.com/zhishenhuang/RLsamp.
