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The place/grid/etc cells fall generally under the topic of cognitive mapping. And people have certainly tried to use it in A.I. over the decades, including recently when the neuroscience won the Nobel prize. But in the niches where it's an obvious thing to try, if you can't even beat ancient ideas like Kalman and particle filters, people give up and move on. Jobs where you make models that don't do better at anything except to show interesting behavior are computational neuroscience jobs, not machine learning, and are probably just as rare as any other theoretical science research position.

There is a niche of people trying to combine cognitive mapping with RL, or indeed arguing that old RL methods are actually implemented in the brain. But it looks like they don't much benefit to show in applications for it. They seem to have no shortage of labor or collaborators at their disposal to attempt and test models. It certainly must be immensely simpler than rat experiments.

Having said that, yes I do believe that progress can come considering how nature accomplish the solution and what major components we are still missing. But common-sense-driven tacking them on there has certainly been tried.

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