my experience of building this repo locally on Ubuntu 16.04

[johnny-wang16]

Hello there, thanks for doing this research project and open source your result. I found it super interesting and decided to try it on my own. Since you guys didn't talk much about building this repo locally, I thought I'd just share my experience here.

I checked the dependencies in the Dockerfile and sudo apt-get from line 14 to 34. Namely:

sudo apt-get install build-essential \
    curl \
    ffmpeg \
    git \
    libgl1-mesa-dev \
    libgl1-mesa-glx \
    libglew-dev \
    libosmesa6-dev \
    net-tools \
    parallel \
    python3.7 \
    python3.7-dev \
    python3-pip \
    rsync \
    software-properties-common \
    unzip \
    vim \
    virtualenv \

And, it turns out that the package manager cannot find python3.7 related packages (python3.7, python3.7-dev, python3-pip) so I installed them based on this link. Namely,

sudo apt install software-properties-common
sudo add-apt-repository ppa:deadsnakes/ppa
sudo apt update
sudo apt install python3.7
sudo apt install python3.7-dev
sudo apt install python3-pip

We'll also need cffi. Install it by pip3 install cffi.
The next step is to install MUJOCO. I installed it based on this link.

1. get a valid license through this link
2. Download MUJOCO pro from here. You'll need mjpro131 and mujoco200 .
3. Unzip mjpro131 and mujoco200 by:

unzip mjpro131_linux.zip -d ~/.mujoco
unzip mujoco200_linux.zip -d ~/.mujoco

4. move the mjkey.txt file that should be sent to your email to the hidden folder ~/.mujoco by mv DOWNLOAD_LOCATION/mjkey.txt ~/.mujoco
5. put the following lines into your bashrc file.

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/home/(username)/.mujoco/mjpro131/bin

I suspect that I'll need to change the environment variable to point to .../mojoco200/bin when training with other gym environment but the above works for replicating one of the experiments in the paper ( python -m aprl.train with env_name=multicomp/SumoHumans-v0 paper )

6. execute step 9 and 10 in the tutorial above.

That's it for installing MUJOCO. The next step is just training the agent.

1. As indicated in the README file of this repo, after cloning this repo, run ci/build_venv.sh , activate it by . ./venv/bin/activate , run pip install -e .

2. As indicated in the README file of this repo, train with python -m aprl.train with env_name=multicomp/SumoHumans-v0 paper

[AdamGleave]

Thanks for the guide. We emphasized Docker in the README over a local install since it's more replicable, but a local install is certainly convenient for development, so I'm sure this will be useful to people.

[johnny-wang16]

Thank you. I was having some issues with Docker not recognizing my student MUJOCO license. I did a bit of searching and found out the hardware locking mechanism in MUJOCO does not work with Docker as stated in this discussion. That's why I attempted to build it locally.