Planning from Pixels using Inverse Dynamics Models

Keiran Paster · Sheila McIlraith · Jimmy Ba

Keywords: [ deep learning ] [ deep reinforcement learning ] [ multi-task learning ] [ model based reinforcement learning ] [ goal-conditioned reinforcement learning ]


Learning dynamics models in high-dimensional observation spaces can be challenging for model-based RL agents. We propose a novel way to learn models in a latent space by learning to predict sequences of future actions conditioned on task completion. These models track task-relevant environment dynamics over a distribution of tasks, 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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