  In analogy to compressed sensing, which allows sample-efficient signal
reconstruction given prior knowledge of its sparsity in frequency domain, we
propose to utilize policy simplicity (Occam's Razor) as a prior to enable
sample-efficient imitation learning. We first demonstrated the feasibility of
this scheme on linear case where state-value function can be sampled directly.
We also extended the scheme to scenarios where only actions are visible and
scenarios where the policy is obtained from nonlinear network. The method is
benchmarked against behavior cloning and results in significantly higher scores
with limited expert demonstrations.
