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Exploring in environments with high-dimensional observations is hard. Onepromising approach for exploration is to use intrinsic rewards, which oftenboils down to estimating "novelty" of states, transitions, or trajectories withdeep networks. Prior works have shown that conditional prediction objectivessuch as masked autoencoding can be seen as stochastic estimation ofpseudo-likelihood. We show how this perspective naturally leads to a unifiedview on existing intrinsic reward approaches: they are special cases ofconditional prediction, where the estimation of novelty can be seen aspseudo-likelihood estimation with different mask distributions. From this view,we propose a general framework for deriving intrinsic rewards -- Masked InputModeling for Exploration (MIMEx) -- where the mask distribution can be flexiblytuned to control the difficulty of the underlying conditional prediction task.We demonstrate that MIMEx can achieve superior results when compared againstcompetitive baselines on a suite of challenging sparse-reward visuomotor tasks.

MIMEx: Intrinsic Rewards from Masked Input Modeling