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

This paper nicely bridges between neuroscience and RL, and considers the important topic of limited memory resources in RL agents. The topic is well-suited for NeurIPS (R2) as it has broader applicability toward e.g. All reviewers agreed that it is well-motivated and written (R1, R2, R3, R4), although R3 did ask for a bit more explanation on some methodological details. It is also appropriately situated with respect to related work (R1, R2, R3) although R2 suggests a separate related works section, and R4 wanted to see more discussion of work outside of neuroscience, focused on optimizing RL with limited capacity. R1 pointed out that perhaps there's a bit of confusion between memory precision and use of memory resources, as the former is more accurate for agents, the latter perhaps for real brains - ie more precise representations require more resources to encode in the brain, but this seems to be a minor point.