Hierarchical RL for Real-Time 3D Content Generation

# Hierarchical RL for Real-Time 3D Content Generation ## Description This project aims to explore the hypothesis that hierarchical reinforcement learning (HRL) strategies can enhance the efficiency and quality of real-time procedural 3D content generation from textual descriptions. The project involves developing a hierarchical RL model to break down the 3D generation task into sub-tasks, each managed by a separate RL agent, and using a real-time feedback loop for continuous improvement. ## Research Hypothesis Hierarchical reinforcement learning strategies can improve the efficiency and quality of real-time procedural 3D content generation from textual descriptions. ## Implementation Approach - Develop a custom hierarchical RL framework. - Build a pipeline for text-to-3D conversion. - Implement a real-time feedback loop for model improvement. - Benchmark the model's performance against existing methods. ## Setup Instructions 1. Clone the repository: ```bash git clone https://github.com/yourusername/hrl_3d_generation.git cd hrl_3d_generation ``` 2. Install the required dependencies: ```bash pip install -r requirements.txt ``` ## Usage Examples Train the hierarchical RL model: ```bash python src/train.py ``` Evaluate the model's performance: ```bash python src/evaluate.py ``` ## Expected Results - Improved generation time and visual fidelity of 3D models. - Real-time feedback integration leading to iterative model enhancement. ## References - [Are We Ready for RL in Text-to-3D Generation? A Progressive Investigation](http://arxiv.org/abs/2512.10949v1)