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Planning from Pixels using Inverse Dynamics Models
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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.
Forward citations
Cited by 2 Pith papers
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MGDA: Model-based Goal Data Augmentation for Offline Goal-conditioned Weighted Supervised Learning
A model-based goal augmentation method, MGDA, improves the stitching ability of offline goal-conditioned weighted supervised learning on maze benchmarks by filtering augmented goals through a locally Lipschitz dynamics model.
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