Linux Training Guide

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

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

Install conda

Open command prompt

wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh

Press ENTER or type yes when prompted

Create conda environment

Open command prompt 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_linux.git
cd mouse_vs_ai_linux

Then, create and activate the conda environment:

conda env create -n mouse -f mouse_linux.yml
conda activate mouse

💡 Troubleshooting: if the CUDA version isn’t compatible with your GPU, please try:

pip install torch==1.8.1+cu111 -f https://download.pytorch.org/whl/torch_stable.html

Set executable permissions for Linux binariesPermalink

To make the Linux executable files executable:

chmod +x ./Builds/RandomTrain/LinuxHeadless.x86_64

# If you have other build directories, make their executables executable too. For example:
# chmod +x ./Builds/RandomTest/LinuxHeadless.x86_64

Modify file path

Open train.py and go to 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/env/mouse2/Lib/site-packages/mlagents/trainers/torch/encoders.py

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, MyNetwork2

• 💡 Troubleshooting: If training only proceeds after pressing ENTER, try running the command with unbuffered output mode: python -u train.py [options]
• If the issue persists, stop the current training episode and train again

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

Customize the model

• To add architecture:
  • Add your model (e.g., MyNetwork1.py) to the /mouse_vs_ai_linux/Encoders directory
  • To train your custom network, run python train.py --network MyNetwork1 [options]
• To adjust hyperparamters:
  • Edit parameters in /mouse_vs_ai_linux/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.