RESEARCH

Plan2vec: Unsupervised Representation Learning by Latent Plans

May 08, 2020

Abstract

In this paper we introduce plan2vec, an unsupervised representation learning approach that is inspired by reinforcement learning. Plan2vec constructs a weighted graph on an image dataset using near-neighbor distances, and then extrapolates this local metric to a global embedding by distilling path-integral over planned path. When applied to control, plan2vec offers a way to learn goal-conditioned value estimates that are accurate over long horizons that is both compute and sample efficient. We demonstrate the effectiveness of plan2vec on one simulated and two challenging real-world image datasets. Experimental results show that plan2vec successfully amortizes the planning cost, enabling reactive planning that is linear in memory and computation complexity rather than exhaustive over the entire state space. Additional results and videos can be found at https://geyang.github.io/plan2vec.

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AUTHORS

Written by

Roberto Calandra

Amy Zhang

Ari Morcos

Joelle Pineau

Ge Yang

Pieter Abbeel

Publisher

Learning for DynamIcs & Control (L4DC)

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