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This paper studies Imitation Learning from Observations alone (ILFO) wherethe learner is presented with expert demonstrations that consist only of statesvisited by an expert (without access to actions taken by the expert). Wepresent a provably efficient model-based framework MobILE to solve the ILFOproblem. MobILE involves carefully trading off strategic exploration againstimitation - this is achieved by integrating the idea of optimism in the face ofuncertainty into the distribution matching imitation learning (IL) framework.We provide a unified analysis for MobILE, and demonstrate that MobILE enjoysstrong performance guarantees for classes of MDP dynamics that satisfy certainwell studied notions of structural complexity. We also show that the ILFOproblem is strictly harder than the standard IL problem by presenting anexponential sample complexity separation between IL and ILFO. We complementthese theoretical results with experimental simulations on benchmark OpenAI Gymtasks that indicate the efficacy of MobILE. Code for implementing the MobILEframework is available at https://github.com/rahulkidambi/MobILE-NeurIPS2021.

MobILE: Model-Based Imitation Learning From Observation Alone