- Internet connection, the faster the better
- Around 2GB free for the
- sudo rights
An automated installation script is provided as install_script.sh which supports Ubuntu (and CentOS Linux but untested).
To have a smooth install, make sure to have installed Anaconda supporting python 3.6 version. The script will install:
- The Torcs binaries and required dependencies
- Anaconda Virtual Environment management and the required virtual env
!!! Important: Before building the vtorcs-RL-color, edit the gym_torqs/vtorcs-RL-colors/src/interfaces/graphic.h, and change the z3r0 in /home/z3r0/... with your Linux user username.
Run source install_script.sh to install most of the required elements.
In case it doesn't work, consider checking out the content of the script anbd following each step manually.
Also requires manual setting of environment variables
export TORCS_DATA_DIR="/home/$USER/torcs_data/" # Used to record player data, make sure the folder structure exists
export DISPLAY=:1 # Used by xvfb-run for headless training, namely on the server
Activate the previously created virtual env with
conda activate baselines-torcs
python -m baselines.torcs_ddpg.main
The result will be dumped in ./baselines/torcs_ddpg/result
python -m baselines.gail.run_mujoco
The result will be dumped in ./baselines/gail/result
python -m baselines.remi.run_mujoco
The result will be dumped in ./baselines/remi/result
Creates shortcuts for fast training and checking. (Preferably add them to your ~/.bashrc file)
alias xvfb-torcs-ddpg-train="xvfb-run -a -s \"-screen $DISPLAY 640x480x24\" python -m baselines.ddpg_torqs.main" # Training
alias xvfb-torcs-ddpg-rec="xvfb-run -a -s \"-screen $DISPLAY 640x480x24\" python -m baselines.ddpg_torqs.record_data" # Recod data
alias xvfb-torcs-gail-train="xvfb-run -a -s \"-screen $DISPLAY 640x480x24\" python -m baselines.gail.run_mujoco" #Training
alias xvfb-torcs-gail-eval="xvfb-run -a -s \"-screen $DISPLAY 640x480x24\" python -m baselines.gail.gail-eval-torcs" # Evaluating
alias xvfb-torcs-remi-train="xvfb-run -a -s "-screen $DISPLAY 640x480x24" python -m baselines.remi.run_mujoco" # Training alias xvfb-torcs-remi-eval="xvfb-run -a -s "-screen $DISPLAY 640x480x24" python -m baselines.remi.remi-eval" # Eval
python -m baselines.torcs_ddpg.play --checkpoint=/path/to/.../torcs-ddpg-XXXX-XX-XX-XX-XX-XXX-XXXXXX/model_data/epoch_XXXX.ckpt
python -m baselines.gail.play --load_model_path=baselines/gail/.../checkpoint/torcs_gail/torcs_gail_XXXX
python -m baselines.remi.play --load_model_path=baselines/remi/result/.../checkpoint/torcs_remi/torcs_remi_XXXX
Add the following to ~/.bashrc, and reload it.
alias torcs-ddpg-play="python -m baselines.ddpg_torqs.play" # DDPG
alias gail-torqs-play="python -m baselines.gail.play" # GAIL
alias remi2-torqs-play="python -m baselines.remi.play" # Hybrid (ReMi)