Semi-supervised Differentiable Particle Filters

This repository provides the source code for semi-supervised differentiable particle filters described in the paper "End-to-end semi-supervised learning for differentiable particle filters".

Prerequisites

Python packages

To install the required python packages, run the following command:

pip install -r requirements.txt

Datasets

Download the dataset for Maze environments to the ./data/ folder from the link: https://tubcloud.tu-berlin.de/s/0fU32cq0ppqdGXe/download.

Project & Script Descriptions

In the main repository folder, the following command needs to append the parent directory to the PYTHONPATH.

export PYTHONPATH="${PYTHONPATH}:../"

Then you can train and test the semi-supervised differentiable particle filters in Maze environments by running the following commands:

cd experiments; python main.py

Scripts

Here are the descriptions for the scipts.

• ./experiments/main.py the main file for the training and test of SemiDPF.
• ./methods/dpf.py the implementations of SemiDPF.
• ./methods/rnn.py the implementations of LSTM baseline algorithm.
• ./utils/data_utils.py the utility function to preprocess the input data.
• ./utils/exp_utils.py the utility function for experiment setup.
• ./utils/method_utils.py some auxiliary functions for implementation of SemiDPF.
• ./utils/plotting_utils.py the utility function for plotting the experiment results.

References

• A large amount of the SemiDPF code and folder structure was based on the implementation of Rico Jonschkowski tu-rbo/differentiable-particle-filters
• Particle Filter Networks
• Differentiable Particle Filters: End-to-End Learning with Algorithmic Priors
• Particle filter networks with application to visual localization