Skip to content


Switch branches/tags

Name already in use

A tag already exists with the provided branch name. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Are you sure you want to create this branch?
This branch is 1 commit behind YunzhuLi:master.

Latest commit


Git stats


Failed to load latest commit information.
Latest commit message
Commit time

InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations

By Yunzhu Li, Jiaming Song, Stefano Ermon


Modified codebase of TORCS, with the ability to extract dashboard views.

InfoGAIL implementation, attached with two examples: pass & turn.

Citing InfoGAIL

If you find this codebase useful in your research, please consider citing:

    title={InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations},
    author={Li, Yunzhu and Song, Jiaming and Ermon, Stefano},
    journal={arXiv preprint arXiv:1703.08840},


  1. Python 2.7
  2. Tensorflow 0.12.1
  3. Keras 1.2.2
  4. xautomation

Install and Configure TORCS

In the following section, we only show the commands for running example 0 (indicating pass). Users can replace 0 with 1 to run example 1 (indicating turn).

  1. Install all the dependencies of TORCS, including but not limited to:
sudo apt-get install libplib-dev
sudo apt-get install libopenal-dev
sudo apt-get install libalut-dev

See for more information

  1. cd to the torcs-1.3.4/ folder, type the following commands to install TORCS:
sudo make install
sudo make datainstall

Default installation directories are:


Run the torcs command to play TORCS.

  1. Copy the modified tracks files in the modified_tracks/ folder to the torcs folder /usr/local/share/games/torcs/tracks/road
  2. Type the following commands to configure the running environment:
rm -rf ~/.torcs
cp -r torcs_config_ai_0 ~/.torcs

Download training data and pretrained weights

  1. Training data
  1. Pretrained weights

Run with pretrained weights

  1. cd to wgail_info_0/

  2. open and edit line 20-21 in concert with downloaded data and weights

  3. change variable code in line 14 into different values (0 or 1) to observe different behaviors

  4. type the python to run pass with pretrained weights

    Run pass with different latent codes (0 or 1):

    Run turn with different latent codes (0 or 1):


  1. cd to wgail_info_0/
  2. open and edit line 17-19 in concert with downloaded data and weights
  3. open and edit line 508 to specify the place to store the weights, edit line 422 to specify the place to store the log file
  4. type python to train on pass, note that the weights trained via behavior cloning are used to initialize the policy network


  1. Track selection:

    • pass: chenyi-Street 1
    • turn: chenyi-Brondehach
  2. torcs-1.3.4 in this repository is a modified version of the original codebase released by Bernhard Wymann. This version holds the ability of extracting and transmitting visual information.



Using Keras and Deep Deterministic Policy Gradient to play TORCS

TORCS - The Open Race Car Simulator


Simulated Car Racing Championship Competition Software Manual


Source code for our NIPS 2017 paper, InfoGAIL: Interpretable Imitation Learning from Visual Demonstrations







No releases published


No packages published


  • C++ 51.7%
  • C 16.8%
  • HTML 10.0%
  • Batchfile 6.2%
  • Makefile 5.3%
  • Roff 4.1%
  • Other 5.9%