Plan-Space State Embeddings for Improved Reinforcement Learning

30 Apr 2020Max PfluegerGaurav S. Sukhatme

Robot control problems are often structured with a policy function that maps state values into control values, but in many dynamic problems the observed state can have a difficult to characterize relationship with useful policy actions. In this paper we present a new method for learning state embeddings from plans or other forms of demonstrations such that the embedding space has a specified geometric relationship with the demonstrations... (read more)

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