Review for NeurIPS paper: Dynamic allocation of limited memory resources in reinforcement learning

Weaknesses: (This section is being combined with "comments for improvement" section below) A bit of a nitpick regarding the language use around "more" or "less" resources. The authors write about an agent using "more resources", which corresponds to "lower entropy" for the actions in a particular state. I think, though, that technically (and literally, for this agent) the amount of resources used for each memory is exactly the same; it's literally a number to represent the mean and standard deviation. From what I can tell, the authors are arguing that memories with lower standard deviations would *require* more resources to represent in certain implementations (such as in brains). So it's not actually the case that more resources are used for low-sigma memories in the agent, but that more resources might be used in other agents.