Procedural RL PCG

# Procedural RL PCG ## Description This project explores the use of reinforcement learning (RL) frameworks adapted from text-to-3D generation to enhance the realism and complexity of procedural content generation (PCG) in video games. Our system employs hierarchical optimization strategies to dynamically create engaging game levels, characters, and objects. ## Research Hypothesis Reinforcement learning frameworks used in text-to-3D generation can be adapted to enhance the realism and complexity of procedural content generation in video games. ## Implementation Approach The system is designed using Python and leverages libraries such as PyTorch and stable-baselines3. A custom game environment is created to simulate player interactions, and a hierarchical RL agent is trained to optimize content generation. ## Setup Instructions 1. Clone the repository: ```bash git clone https://github.com/yourusername/procedural_rl_pcg.git cd procedural_rl_pcg ``` 2. Install the required packages: ```bash pip install -r requirements.txt ``` 3. Set up your data sources in the `data/` directory. ## Usage Examples - Train the RL agent: ```bash python src/train.py ``` - Evaluate the generated content: ```bash python src/evaluate.py ``` ## Expected Results The system aims to generate game content that is both complex and realistic, enhancing player immersion and creativity. ## References - [Are We Ready for RL in Text-to-3D Generation? A Progressive Investigation](http://arxiv.org/abs/2512.10949v1)