Planning from Pixels using Inverse Dynamics Models

Keiran Paster; Sheila A. McIlraith; Jimmy Ba

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arXiv · 2012.02419

Planning from Pixels using Inverse Dynamics Models

Keiran Paster·Sheila A. McIlraith·Jimmy Ba

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Abstract

Learning task-agnostic dynamics models in high-dimensional observation spaces can be challenging for model-based RL agents. We propose a novel way to learn latent world models by learning to predict sequences of future actions conditioned on task completion. These task-conditioned models adaptively focus modeling capacity on task-relevant dynamics, while simultaneously serving as an effective heuristic for planning with sparse rewards. We evaluate our method on challenging visual goal completion tasks and show a substantial increase in performance compared to prior model-free approaches.

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Keiran Paster, Sheila A. McIlraith, Jimmy Ba. 2020-12-04. Planning from Pixels using Inverse Dynamics Models. https://arxiv.org/abs/2012.02419

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