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learning_to_plan

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Code for paper "Learning to Plan with Uncertain Topological Maps"

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// repository documentation

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# Learning to Plan with Uncertain Topological Maps

This repository contains code for paper [Learning to Plan with Uncertain Topological Maps]( https://arxiv.org/abs/2007.05270) ECCV 2020 (Spotlight)

# Contents
* Requirements
* Dataset
* Training
* Pretrained model
* Citation

## Requirements:
pip install -R requirements.txt

## Dataset 
The dataset is make available on Zenodo https://zenodo.org/record/4003445
The dataset is 4.8 GB in size

```
mkdir data && cd data
wget -O graph_data3_distance_weights.gz  https://zenodo.org/record/4003445/files/file.data?download=1 
tar -xvzf graph_data3_distance_weights.gz

```
## Training

Please use the following hyperparameters for training:

```
python train.py --data_path data/graph_data3_distance_weights/train --hidden_size 256 --batch_size 32 --max_grad_norm 2 --weight_decay 0.0001 --lr 0.001 --schedule 120 --n_steps 6 --data_limit 74000 --use_weights --normalize --gru_depth 2 --save_model --bound_update --new_bound_net --store_hidden --use_probs --use_features

```

## Pretrained model
The pretrained model can be found in models/best_model.pth

## Citation

If you find this useful, consider citing the following:
```
@inproceedings{beeching2020learntoplan,
  title={Learning to plan with uncertain topological maps.
  },
  author={Beeching, Edward and Dibangoye, Jilles and 
          Simonin, Olivier and Wolf, Christian}
  booktitle={European Conference on Computer Vision},
  year={2020}}
```

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