SKILL-IL: Disentangling Skill and Knowledge in Multitask Imitation Learning. (arXiv:2205.03130v1 [cs.LG])

Web: http://arxiv.org/abs/2205.03130

In this work, we introduce a new perspective for learning transferable
content in multi-task imitation learning. Humans are able to transfer skills
and knowledge. If we can cycle to work and drive to the store, we can also
cycle to the store and drive to work. We take inspiration from this and
hypothesize the latent memory of a policy network can be disentangled into two
partitions. These contain either the knowledge of the environmental context for
the task or the …

arxiv imitation learning knowledge learning