Complex Skill Acquisition through Simple Skill Adversarial Imitation Learning

Humans are able to think of complex tasks as combinations of simpler subtasks in order to learn the 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 adversarial imitation learning of complex, hard-to-learn skills. We evaluate our algorithm on a difficult task in a high-dimensional environment and see that it consistently outperforms a state-of-the-art baseline in training speed and overall task performance.