  In X-ray Computed Tomography (CT), projections from many angles are acquired
and used for 3D reconstruction. To make CT suitable for in-line quality
control, reducing the number of angles while maintaining reconstruction quality
is necessary. Sparse-angle tomography is a popular approach for obtaining 3D
reconstructions from limited data. To optimize its performance, one can adapt
scan angles sequentially to select the most informative angles for each scanned
object. Mathematically, this corresponds to solving and optimal experimental
design (OED) problem. OED problems are high-dimensional, non-convex, bi-level
optimization problems that cannot be solved online, i.e., during the scan. To
address these challenges, we pose the OED problem as a partially observable
Markov decision process in a Bayesian framework, and solve it through deep
reinforcement learning. The approach learns efficient non-greedy policies to
solve a given class of OED problems through extensive offline training rather
than solving a given OED problem directly via numerical optimization. As such,
the trained policy can successfully find the most informative scan angles
online. We use a policy training method based on the Actor-Critic approach and
evaluate its performance on 2D tomography with synthetic data.
