  Imitation from observation is a computational technique that teaches an agent
on how to mimic the behavior of an expert by observing only the sequence of
states from the expert demonstrations. Recent approaches learn the inverse
dynamics of the environment and an imitation policy by interleaving epochs of
both models while changing the demonstration data. However, such approaches
often get stuck into sub-optimal solutions that are distant from the expert,
limiting their imitation effectiveness. We address this problem with a novel
approach that overcomes the problem of reaching bad local minima by exploring:
(I) a self-attention mechanism that better captures global features of the
states; and (ii) a sampling strategy that regulates the observations that are
used for learning. We show empirically that our approach outperforms the
state-of-the-art approaches in four different environments by a large margin.
