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In this paper we introduce plan2vec, an unsupervised representation learningapproach that is inspired by reinforcement learning. Plan2vec constructs aweighted graph on an image dataset using near-neighbor distances, and thenextrapolates this local metric to a global embedding by distillingpath-integral over planned path. When applied to control, plan2vec offers a wayto learn goal-conditioned value estimates that are accurate over long horizonsthat is both compute and sample efficient. We demonstrate the effectiveness ofplan2vec on one simulated and two challenging real-world image datasets.Experimental results show that plan2vec successfully amortizes the planningcost, enabling reactive planning that is linear in memory and computationcomplexity rather than exhaustive over the entire state space.

Plan2Vec: Unsupervised Representation Learning by Latent Plans