Plan2Vec: Unsupervised Representation Learning by Latent Plans

Made for a reading group at the Center for Safe AGI.

Plan2Vec: Unsupervised Representation Learning by Latent Plans

Ge Yang, Amy Zhang, Ari S. Morcos, Joelle Pineau, Pieter Abbeel, Roberto Calandra


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.

code paper