fj198602/001/experiments - Storage Bucket

Evaluation Experiments

This section provides instructions for setting up the evaluation environments.

📂 Expected Directory Structure

To ensure the scripts can locate the environments, please organize your files as follows:

SkillNet/
├── experiments/
│   ├── alfworld/          # git clone here
│   ├── ScienceWorld/      # git clone here
│   ├── WebShop/           # git clone here
│   ├── src/
│   ├── requirements.txt
│   ├── alfworld_run.py
│   ├── scienceworld_run.py
│   └── webshop_run.py

🚀 Quick Start

We suggest configuring separate conda environments for these three datasets to avoid dependency conflicts.

ALFWorld

1. Clone & Setup:

cd experiments
git clone https://github.com/alfworld/alfworld.git
cd alfworld
# Follow the official installation steps from the repo (https://github.com/alfworld/alfworld)

2. Environment Variable:

• Set ALFWORLD_DATA to the dataset root or edit src/alfworld/base_config.yaml to point to your local paths:

  export ALFWORLD_DATA=/path/to/alfworld_data
  

ScienceWorld

1. Clone & Setup:

cd experiments
git clone https://github.com/allenai/ScienceWorld.git
cd ScienceWorld
# Refer to the ScienceWorld repository for environment setup (https://github.com/allenai/ScienceWorld)

WebShop

1. Clone & Setup:

cd experiments
git clone https://github.com/princeton-nlp/WebShop.git
cd WebShop
# Refer to the WebShop repository for environment setup (https://github.com/princeton-nlp/WebShop)

---

For each environment, install common dependencies:

cd experiments
pip install -r requirements.txt

Running

Step 1: Initialize Environment Variables

Before running the scripts, configure your API credentials:

export API_KEY=YOUR_API_KEY
export BASE_URL=YOUR_API_BASE_URL

Step 2: Execution

Run the corresponding evaluation script from the experiments/ directory.

cd experiments

# ALFWorld
python alfworld_run.py --model o4-mini --split dev --max_workers 10 --exp_name alf_test --use_skill

# ScienceWorld
python scienceworld_run.py --model o4-mini --split test --max_workers 5 --exp_name sci_test --use_skill

# WebShop
python webshop_run.py --model o4-mini --max_workers 3 --exp_name web_test --use_skill

🛠️ Argument Descriptions

• --model: The name of the LLM to evaluate.

• --split: Data split to use (dev or test).

• --max_workers: Number of parallel workers for evaluation.

• exp_name: results save name.

• --use_skill: Enable the skill-augmented module.