Reviews: Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck

This work builds on the previous work about generalization in RL ([10] in the paper references) by (re-)investigating the classical stochastic regularization approaches in this context. It completes and updates the claims made in [10] by focusing of similar performance based experiments. Clarity: The method is clearly described in the paper. Significance: The question of generalization in RL is of great interest to the field. Main comments: - The paper motivates well the problems one faces when is comes to regularization in RL.