Reviews: Shaping Belief States with Generative Environment Models for RL

This paper examines the use of generative models for developing representations to improve data efficiency in RL. Specifically, the authors use a generative model that is trained to predict multiple frames into the future (overshooting), and they show that when the model is stochastic (but not deterministic), overshooting leads to useful representations of the environment that can improve RL efficiency. The reviews on this paper were fairly divergent in the first round. Two of the reviewers liked this paper, but one did not feel it provided truly novel contributions, and only brought together previously proposed ideas for using predictive training to improve RL representations. In discussion, the reviewers came to the conclusion that it does demonstrate the utility of overshoot prediction for stochastic models and that an empirical demonstration like this can be useful.