Plan2Vec: Unsupervised Representation Learning by Latent Plans

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

Abstract: In this paper, we introducePlan2Vec, an model-based method to learn state representation fromsequences of off-policy observation data via planning. In contrast to prior methods, plan2vec doesnot require grounding via expert trajectories or actions, opening it up to many unsupervised learningscenarios. When applied to control, plan2vec learns a representation that amortizes the planningcost, enabling test time planning complexity that is linear in planning depth rather than exhaustiveover the entire state space. We demonstrate the effectiveness of Plan2Vec on one simulated andtwo real-world image datasets, showing that Plan2Vec can effectively acquire representations thatcarry long-range structure to accelerate planning. Additional results and videos can be found at https://sites.google.com/view/plan2vec

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/arxiv:2005.03648/code)