Successor from meloetta, hence porygon 2

Porygon2 provides:

• A UNIX server written in Node.js wrapped around the sim and client packages from pkmn.
• A reinforcement learning framework for interacting with this server. Currently supports R-NaD from DeepMind.

Porygon2 is a platform that simulates Pokémon battles and provides an environment for training reinforcement learning agents. It leverages the pkmn library for accurate game mechanics and offers a server-client architecture to facilitate interactions between agents and the simulation environment.

To set up the project, you can use the provided setup.sh script, which automates the installation process.

• Python 3 installed on your system.
• Node.js and npm installed for running the server components.

1. Clone the Repository

git clone https://github.com/yourusername/porygon2.git
cd porygon2

2. Run the Setup Script

Make sure the setup.sh script is executable:

chmod +x setup.sh

Then run the script:

./setup.sh

This script will:

• Create a Python virtual environment in the venv directory.
• Install all Python dependencies from requirements.txt files located in the root directory and immediate subdirectories, excluding any directories named env.
• Install all Node.js dependencies by running npm install in directories (one layer deep) containing a package.json file, excluding any directories named env or node_modules.

3. Activate the Python Virtual Environment

After running the script, your Python virtual environment is activated. If you open a new terminal session, reactivate it using:

source venv/bin/activate

1. Navigate to the Server Directory

cd service

2. Install Dependencies

npm install

3. Run Tests (Optional)

npm run test

4. Start the Training Server

npm run start

Open a new terminal and navigate to the root directory of the repository.

1. Activate the Virtual Environment

source venv/bin/activate

2. Run the Training Client

python ml/rl.py

1. Activate the Virtual Environment

source venv/bin/activate

2. Start the Inference Server

python inference/server.py

1. Navigate to the Server Directory

cd server

2. Install Dependencies

npm install

3. Start the Evaluation Client

npm run start-evaluation-client

The scripts/ directory contains helper scripts for various tasks:

• compile_protos.sh: Compiles protocol buffer definitions.
• generate_requirements.sh: Generates requirements.txt files.
• lint.sh: Runs code linters to ensure code quality.
• make_data.sh: Generates necessary data for the project.

• Directory Structure: The project contains several subdirectories with their own npm installs. The setup.sh script handles these installations automatically.
• Python Virtual Environment: A virtual environment named env is created in the root directory. Always activate it before running Python scripts.

• License: This project is licensed under the MIT License.