Skip to content

Latest commit

 

History

History
38 lines (32 loc) · 1.49 KB

README.md

File metadata and controls

38 lines (32 loc) · 1.49 KB

Torcs Behavior Cloning

Learn to drive in TORCS from expert example.

How to use

Python dependencies

  • Install anaconda python3 (https://www.anaconda.com/download/#linux)
  • clone this repo.
  • cd into the gym-torcs directory.
  • create a virtual environment with Conda, Anaconda's CLI tool: conda env create -f environment.yml -n sdcc. If you get an error at the end, don't worry about it. (TODO: FIX)
  • activate the environment source activate sdcc
  • install remaining packages through pip pip install -r requirements.txt

Dependencies for the Simulator

  • x-automation7: sudo apt install xautomation
  • other dependencies: sudo apt install libglib2.0-dev libgl1-mesa-dev libglu1-mesa-dev freeglut3-dev libplib-dev libopenal-dev libalut-dev libxi-dev libxmu-dev libxrender-dev libxrandr-dev libpng12-dev ffmpeg

Configuring and installing the simulator

  • cd into gym-torcs/vtorcs-RL-color
  • ./configure
  • make
  • sudo make install
  • sudo make datainstall
  • Go back up a directory (to gym-torcs).
  • Configure permissions with sudo ./configure_permissions

Recording Images with the simulator

  • cd to gym-torcs directory -python snakeoil3_gym.py
  • That will record images in to ./databases/<track>/<trial>/imgs directory.
  • To make images into a movie, use fmpeg on the directory: Ffmpeg -i <path>/imgs/%05d.png video.webm

Training the agent

  • be in gym-torcs directory.
  • assumes you have already recorded some data somewhere.
  • python main.py -d ./databases/<track>/<trial>