  In this work, we introduce a new method for imitation learning from video
demonstrations. Our method, Relational Mimic (RM), improves on previous visual
imitation learning methods by combining generative adversarial networks and
relational learning. RM is flexible and can be used in conjunction with other
recent advances in generative adversarial imitation learning to better address
the need for more robust and sample-efficient approaches. In addition, we
introduce a new neural network architecture that improves upon the previous
state-of-the-art in reinforcement learning and illustrate how increasing the
relational reasoning capabilities of the agent enables the latter to achieve
increasingly higher performance in a challenging locomotion task with pixel
inputs. Finally, we study the effects and contributions of relational learning
in policy evaluation, policy improvement and reward learning through ablation
studies.
