Latent Importance Sampling

This repository contains code and experiments for the manuscript:

Enhanced Importance Sampling through Latent Space Exploration in Normalizing Flows (Accepted, AAAI 2025)

Dependencies

See environment.yaml for required packages, or use this to create a Conda environment with all dependencies:

conda env create -f environment.yaml

This repository was tested with Python 3.11 and PyTorch 2.1. The racecar and f16 experiments require additional installations (discussed in the following section) if you want to retrain a flow model or generate a dataset of Monte Carlo simulations.

Data Generation

To create a dataset (for flow training or Monte Carlo evaluations) for the nonholonomic robot experiments, run the following command:

python3 robot-data.py

Generating data for the racecar experiments requires a Julia install and the MPOPIS (Model Predictive Optimized Path Integral Strategies) repository from the following paper:

@inproceedings{Asmar2023},
  title = {Model Predictive Optimized Path Integral Strategies},
  author = {Dylan M. Asmar and Rasalu Senanayake and Shawn Manuel and Mykel J. Kochenderfer},
  booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
  year = {2023}

Specific installation instructions can be found in the MPOPIS repository.

Generating data for the f16 experiments requires a jax install and the Jax F16 Dynamics repository from the following paper:

@inproceedings{So-RSS-23},
  title = {Solving Stabilize-Avoid Optimal Control via Epigraph Form and Deep Reinforcement Learning}, 
  author = {Oswin So AND Chuchu Fan}, 
  booktitle = {Proceedings of Robotics: Science and Systems}, 
  year = {2023}, 
} 

Specific installation instructions can be found in the Jax F16 repository.

Flow Training

Pre-trained flows are provided in the flows\ folder. However, if you wish to train a flow from scratch, you can run the following command, replacing the simulator argument with the simulator of choice (robot, racecar, f16):

python3 train.py --simulator 'robot'

Flow hyperparameters are stored in yaml files in the configs\ folder.

Latent Importance Sampling Experiments

The experiments expect a {simulator}-flow.csv and {simulator}-mcs.csv file for each simulator. robot-flow.csv and robot-mcs.csv are provided in the repository. The following command runs importance sampling and computes metrics:

python3 experiments.py --simulator 'robot'

Importance sampling hyperparameters are stored in yaml files in the configs\ folder and can be adjusted for different runs. For example, to change between target space sampling and latent space sampling, edit the space entry. Results are stored in the results\ folder.