MacOS Training Guide

Welcome to Mouse vs. AI: Robust Visual Foraging Challenge @ NeurIPS 2025

This is a training guide for MacOS. For other operating systems, please check: Windows and Linux

Install conda

Open the command prompt:

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh -O ~/miniconda.sh
bash ~/miniconda.sh -b -p $HOME/miniconda

In order to initialize after the installation process is done, first run source <path to conda>/bin/activate and then run conda init --all.

Create conda environment

Open Terminal and navigate to the directory where you want to download the project.

Clone the repository from GitHub:

git clone https://github.com/robustforaging/mouse_vs_ai_macOS.git
cd mouse_vs_ai_macOS

Then, create and activate the conda environment:

CONDA_SUBDIR=osx-64 conda env create -n mouse --file mouse.yml
conda activate mouse

You may also need to install pandas separately: pip install pandas

Give permission to a MacOS app

# Make the app's binary executable
chmod +x ./Builds/RandomTrain/RandomTrain.app/Contents/MacOS/*

# Remove the 'quarantine' flag applied by macOS (for downloaded apps)
xattr -dr com.apple.quarantine ./Builds/RandomTrain/RandomTrain.app

❗ Important: Replace ./Builds/RandomTrain/RandomTrain.app with the actual path to your .app bundle in both commands. You need to run these commands for each app you intend to execute if macOS flags it.

Modify file path

Open train.py and go to line 137 (where replace.replace_nature_visual_encoder is called). Update the path to point to the location of encoders.py in your conda environment.

📝 Note: The encoders.py file is usually located in your conda environment’s working directory. For example: /miniconda3/envs/mouse/lib/python3.8/site-packages/mlagents/trainers/torch

Run script

Training

Usage: python train.py [options]

Training options:
  --runs-per-network R    Number of runs per network (default: 5)
  --env ID                Run identifier (default: Normal) [defines 
                          type of environment]
  --network N1,N2,N3      Comma-separated list of networks to train
                          (default choices: ['fully_connected', 
                          'nature_cnn', 'simple', 'resnet'])
                          You can specify your own custom networks here as 
                          well. Just list their names, separated by commas.

Example command for training:

python train.py --runs-per-network 1 --env RandomTrain --network MyNetwork1

Evaluating

Usage: python evaluate.py [options]

Evaluation options:
  --model      Path to the trained ONNX model file
  --episodes   Number of episodes to run in inference(default: 50)
  --env        Build folder name under ./Builds/
  --log-name   Base name for the output log file

Example command for evaluation:

python evaluate.py --model "/path/to/your_model.onnx" --log-name "example.txt" --episodes 10

❗ Important: Replace /path/to/your_model.onnx with the full path to your own ONNX model file on your machine.

Customize the model

• To add architecture:
  • Add your model (e.g., MyNetwork1.py) to the /mouse_vs_ai_macOS/Encoders directory
  • To train your custom network, run python train.py --network MyNetwork1 [options]
• To adjust hyperparamters:
  • Edit parameters in /mouse_vs_ai_macOS/Encoders/nature.yaml file
  • 📝 Note: Please do not change the name of this file or the parameter vis_encode_type in this file. Only modify other configuration values as needed.

After making your changes, run the Python training script as described above.