  We investigate the use of animal videos (observations) to improve
Reinforcement Learning (RL) efficiency and performance in navigation tasks with
sparse rewards. Motivated by theoretical considerations, we make use of
weighted policy optimization for off-policy RL and describe the main challenges
when learning from animal videos. We propose solutions and test our ideas on a
series of 2D navigation tasks. We show how our methods can leverage animal
videos to improve performance over RL algorithms that do not leverage such
observations.
