Abstract:Hierarchical skill learning is an important research direction in human intelligence. However, many real-world problems have sparse rewards and a long time horizon, which typically pose challenges in hierarchical skill learning and lead to the poor performance of naive exploration. In this work, we propose an algorithmic framework called surprise-based hierarchical exploration for model and skill learning (Surprise-HEL). The framework leverages the surprise-based intrinsic motivation for improving the efficien… Show more
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