Direct Future Prediction

This is a re-implementation of Direct Future Prediction in PyTorch.

If you use this code or the provided environments in your research, please cite the following paper:

@inproceedings{DK2017,
author    = {Alexey Dosovitskiy and Vladlen Koltun},
title     = {Learning to Act by Predicting the Future},
booktitle = {International Conference on Learning Representations (ICLR)},
year      = {2017}
}

Sample Results

Small deviations from the paper results are due to:

• the paper shows average and standard deviation over 3 runs.
• in the original code, every evaluation is a result of 50k interaction steps. The code here evaluates over 100 episodes regardless of episode lengths.
• the paper results are produced with a modified version of vizdoom.

Usage

This code base uses sacred to manage configurations and track metrics and artifacts.

You can run the experiments for each scenario with

python launch.py with configs/$SCENARIO_NAME.yaml

where SCENARIO_NAME can be one of {'basic', 'navigation', 'battle', 'battle2'}.

If you want to store configuration, metrics and artifacts in files, use

python launch.py with configs/$SCENARIO_NAME.yaml -F $SAVE_DIR

where SAVE_DIR is the directory path to save under.

For different kinds of storage options (e.g. databases), check sacred's documentation.

Trained Models

Trained networks for each of the 4 scenarios can be found here.

Official Implementation

The official implementation of the paper in tensorflow can be found here.