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Complex Skill Acquisition Through Simple Skill Imitation Learning
[article]
2020
arXiv
pre-print
Humans often think of complex tasks as combinations of simpler subtasks in order to learn those complex tasks more efficiently. For example, a backflip could be considered a combination of four subskills: jumping, tucking knees, rolling backwards, and thrusting arms downwards. Motivated by this line of reasoning, we propose a new algorithm that trains neural network policies on simple, easy-to-learn skills in order to cultivate latent spaces that accelerate imitation learning of complex,
arXiv:2007.10281v4
fatcat:xmeromusp5bvrowwdyo7zrhclq