  This paper studies Imitation Learning from Observations alone (ILFO) where
the learner is presented with expert demonstrations that only consist of states
encountered by an expert (without access to actions taken by the expert). We
present a provably efficient model-based framework MobILE to solve the ILFO
problem. MobILE involves carefully trading off exploration against imitation -
this is achieved by integrating the idea of optimism in the face of uncertainty
into the distribution matching imitation learning (IL) framework. We provide a
unified analysis for MobILE, and demonstrate that MobILE enjoys strong
performance guarantees for classes of MDP dynamics that satisfy certain well
studied notions of complexity. We also show that the ILFO problem is strictly
harder than the standard IL problem by reducing ILFO to a multi-armed bandit
problem indicating that exploration is necessary for ILFO. We complement these
theoretical results with experimental simulations on benchmark OpenAI Gym tasks
that indicate the efficacy of MobILE.
