Reviews: Learning to Predict Without Looking Ahead: World Models Without Forward Prediction

Main Ideas The high-level motivation of this work is to consider alternatives to learning good forward models, which may not be a desirable solution in all cases. The hypothesis is that a predictive model may arise as an emergent property if such prediction were useful for the agent. The authors test this hypothesis by constraining the agent to only observe states at certain timesteps, requiring a model to learn to fill in the gaps. The model was not trained with a forward prediction objective. The method introduced in this work seem novel in the context of other literature that train forward models.