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We found that training an inverse model is crucial for learning good representations. On the first row,alevel from each environment that one-shot PPGS fails tosolve(thewhitearrowsrepresent thepolicy). Iterative Model Improvement In general settings, collecting training trajectories by sampling actions uniformly atrandom does not grant sufficient coverage ofthe state space. GLAMORGLAMOR [34] learns inverse dynamics to achieve visual goals in Atari games. The only difference withPPGS in terms of settings is that we allowGLAMORto collect data on-policy and for more interactions (2M).

Therefore, itmay be necessary to impose aseparate constraint for each such intersection.