Some goal-conditioned environments in unity.
## Installation (Should work on both debian/ubuntu/arch and windows)
- Install anaconda https://www.anaconda.com/download
- Install unity-hub https://docs.unity3d.com/hub/manual/InstallHub.html
- Create a conda environment with the right python version: conda create -n mlagents python=3.10.12 && conda activate mlagents
- Install pytorch, cuda, and mlagents
pip3 install torch~=2.2.1 --index-url https://download.pytorch.org/whl/cu121 pip3 install mlagents
Launch unity hub
Go to projects > Add (top right corner grey button) > Add project from disk, then select the directory called "navigation_envs_unity_project" from this directory. IMPORTANT: Do not select THIS directory (it will just print an error message), select the SUBFOLDER named "navigation_envs_unity_project"
Open the scene of one of the environments (example: ./Assets/PointMaze/Scenes/PointMaze) but selecting it in the file navigator in the bottom-left, and double click on the scene file.
Now, open a terminale, and change the directory to this one.
Activate the conda environment you created earlier,
Launch a training, ex: mlagents-learn config/point_maze_sac.yaml --run-id="my_first_run" Note: The first argument is the config file (hyperparameters and other stuff). Choose the right one according to the scene you choose (in the config directory). Note 2: if you already launched this command, then a run with this id have been saved. Add --force if you want to erase the old one, --resume if you want to resume it, or change the id in the command.
If everything works, you should have a unity logo in your terminal, and a line telling that the program is listening at port 5004. Click on the play button at the top of the unity editor to launch the training.
Wait for the agent to converge!