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I don't think the author rediscovered reinforcement learning -- they just didn't use that phrase or cite much of the prior work there. But they do mention a Bellman-style decayed sum of rewards, and they use the terms 'state' and 'action' and associated notation which follow the conventions in in RL, so I'm pretty sure they're aware of that field.

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Correct. I'm very familiar with RL, but I didn't see how referring to RL literature fit in to my particular argument (and I'm not intimately familiar with that literature). That said, I'd suspect there are fruitful connections to make there, especially considering that this model completely glosses over learning, which is a pretty crucial element...

The K&A paper does mention the connection with RL: "Here, we consider the function of the circuit from the point of view of navigation, a connection anticipated by early work on TD learning (Montague et al, 1995)" (specifically, Montague et al found this connection when building a model of bee foraging).

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