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}
}

• master branch contains the algorithm implementation, example experiment configuration files and ViZDoom environment definitions (.cfg and .wad) - around 10Mb in total
• pretrained_models branch additionally contains pre-trained models for all examples, and corresponding log files - around 200 Mb in total
• Clone only the master branch to avoid extra traffic

• ViZDoom
• numpy
• tensorflow
• OpenCV python bindings
• (optionally, cuda and cudnn)

• Ubuntu 14.04
• python 3.4
• tensorflow 1.0
• ViZDoom master branch commit ed25f236ac93fbe7f667d64fe48d733506ce51f4

• Adjust ViZDoom path in doom_simulator.py

• For testing, switch to the pretrained_models branch and run (using D3 as an example):

    cd examples/D3_battle
    python3 run_exp.py show
  

• For training, run the following (using D2 as an example):

    cd examples/D3_battle
    python3 run_exp.py train
  

• If you have multiple gpus, make sure that only one is visible with

    export CUDA_VISIBLE_DEVICES=NGPU
  

  where NGPU is the number of GPU you want to use, or "" if you do not want to use a gpu

• For speeding things up you may want to prepend "taskset -c NCORE" before the command, where NCORE is the number of the core to be used, for example:

    taskset -c 1 python3 run_exp.py train
  

  When training with a GPU, one core seems to perform the best. Without a GPU, you may want 4 or 8 cores.

• For experiments in the paper we used a slightly modified ViZDoom version which provided a post-mortem measurement. This turns out to make a difference for training. For this reason, the results with this code and the default ViZDoom version may differ slightly from the results in the paper.

• Results may vary across training runs: in our experiments, up to roughly 15%.

• In battle scenarios, the reward provided by ViZDoom is the number of frags. For training the baseline approaches we did not use this reward, but rather a weighted average of the three measurements, same as for our approach, for a fair comparison.

Please send bug reports to Alexey Dosovitskiy adosovitskiy@gmail.com