
Vector-based navigation using grid-like representations in artificial agents - stirbot
https://medium.com/syncedreview/new-deepmind-ai-learns-to-navigate-like-an-animal-scientists-react-963fcca75f65
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apl
It's certainly an interesting paper, but there's a bit of publication
weirdness at play here.

In October '17, Cueva & Wei put out a(n anonymous) paper that recapitulates
the core result almost exactly -- that training a recurrent neural network to
perform dead reckoning/path integration gives you intermediate units whose
place fields strongly resemble grid cells. Critically, this only happens when
regularization is applied; Cueva/Wei used noisy inputs and DeepMind
implemented 50% stochastic dropout in the intermediate linear layer. There are
some superficial differences (generic RNN units vs. LSTM), but at their core
these studies are virtually identical. Check it out:

[https://openreview.net/forum?id=B17JTOe0-](https://openreview.net/forum?id=B17JTOe0-)

What I don't get -- why doesn't DeepMind acknowledge this result? Sure, the
Nature paper was submitted in July '17, but these things go through many
revisions. Clearly, DeepMind went a bit further with the whole integrating
visual CNNs/grid cells part. Nonetheless: Fig. 1 is the core result,
everything from Fig. 2 onwards is nice-to-have but not essential, and I feel
like Cueva/Wei got there first.

Ah, well. At least the minor controversy brings in great publicity for the
Cueva/Wei paper.

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stirbot
Nature news article on the paper:
[https://www.nature.com/articles/d41586-018-04992-7](https://www.nature.com/articles/d41586-018-04992-7)

