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https://github.com/google-deepmind/grid-cells

Implementation of the supervised learning experiments in Vector-based navigation using grid-like representations in artificial agents, as published at https://www.nature.com/articles/s41586-018-0102-6
https://github.com/google-deepmind/grid-cells

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Implementation of the supervised learning experiments in Vector-based navigation using grid-like representations in artificial agents, as published at https://www.nature.com/articles/s41586-018-0102-6

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# Vector-based navigation using grid-like representations in artificial agents



This package provides an implementation of the supervised learning experiments

in Vector-based navigation using grid-like representations in artificial agents,

as [published in Nature](https://www.nature.com/articles/s41586-018-0102-6)



Any publication that discloses findings arising from using this source code must

cite "Banino et al. "Vector-based navigation using grid-like representations in

artificial agents." Nature 557.7705 (2018): 429."



## Introduction



The grid-cell network is a recurrent deep neural network (LSTM). This network

learns to path integrate within a square arena, using simulated trajectories

modelled on those of foraging rodents. The network is required to update its

estimate of location and head direction using translational and angular velocity

signals which are provided as input. The output of the LSTM projects to place

and head direction units via a linear layer which is subject to regularization.

The vector of activities in the place and head direction units, corresponding to

the current position, was provided as a supervised training signal at each time

step.



The dataset needed to run this code can be downloaded from

[here](https://console.cloud.google.com/storage/browser/grid-cells-datasets).



The files contained in the repository are the following:



*   `train.py` is where the training and logging loop happen; The file comes

    with the flags defined in Table 1 of the paper. In order to run this file

    you will need to specify where the dataset is stored and where you want to

    save the results. The results are saved in PDF format and they contains the

    ratemaps and the spatial autocorrelagram order by grid score. The units are

    ordered from higher to lower grid score. Only the last evaluation is saved.

    Please note that given random seeds results can vary between runs.



*   `data_reader.py` read the TFRecord and returns a ready to use batch, which

    is already shuffled.



*   `model.py` contains the grid-cells network



*   `scores.py` contains all the function for calculating the grid scores and

    doing the plotting.



*   `ensembles.py` contains the classes to generate the targets for training of

    the grid-cell networks.



## Train



The implementation requires an installation of

[TensorFlow](https://www.tensorflow.org/) version 1.12, and

[Sonnet](https://github.com/deepmind/sonnet) version 1.27.



```shell

$ virtualenv env

$ source env/bin/activate

$ pip install --upgrade numpy==1.13.3

$ pip install --upgrade tensorflow==1.12.0-rc0

$ pip install --upgrade dm-sonnet==1.27

$ pip install --upgrade scipy==1.0.0

$ pip install --upgrade matplotlib==1.5.2

$ pip install --upgrade tensorflow-probability==0.5.0

$ pip install --upgrade wrapt==1.9.0

```



An example training script can be executed from a python interpreter:



```shell

$ python train.py --task_root='path/to/datasets/root/folder' --saver_results_directory='path/to/results/folder'

```



Disclaimer: This is not an official Google product.