https://github.com/shiroonigami23-ui/omega

Beautiful modular D3QN research pipeline with training, ablations, plots, report, and packaging
https://github.com/shiroonigami23-ui/omega

ablation-study d3qn gymnasium pytorch reinforcement-learning research-pipeline

Last synced: about 2 months ago
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Beautiful modular D3QN research pipeline with training, ablations, plots, report, and packaging

• Host: GitHub
• URL: https://github.com/shiroonigami23-ui/omega
• Owner: shiroonigami23-ui
• Created: 2026-04-29T18:56:10.000Z (2 months ago)
• Default Branch: main
• Last Pushed: 2026-04-30T23:30:40.000Z (2 months ago)
• Last Synced: 2026-05-15T02:59:59.523Z (about 2 months ago)
• Topics: ablation-study, d3qn, gymnasium, pytorch, reinforcement-learning, research-pipeline
• Language: Python
• Size: 5.61 MB
• Stars: 2
• Watchers: 0
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

README

          
# Project Omega: Full Delivery Repository

![Python](https://img.shields.io/badge/Python-3.11-blue)

![RL](https://img.shields.io/badge/Reinforcement%20Learning-D3QN-success)

![Status](https://img.shields.io/badge/Status-Delivery--Packaged-brightgreen)

This repository contains the full packaged Project Omega delivery and extracted artifacts.

## Included Delivery

- `delivery_full/source_code/` complete source modules

- `delivery_full/plots/` performance, distributions, ablation

- `delivery_full/video/` agent GIFs

- `delivery_full/reports/` research paper PDF + executive PPTX

- `delivery_full/models/checkpoints/` saved checkpoints

- `delivery_full/output/Project_Omega_Release.zip` packaged final artifact

## Visual Preview

![D3QN GIF](delivery_full/video/best_agent_d3qn.gif)

![Performance Curve](delivery_full/plots/performance/performance_curve.png)

![Ablation](delivery_full/plots/ablation/ablation_comparison.png)

## Kaggle Save + Version

1. Create a Kaggle Dataset and upload `Project_Omega_FULL_DELIVERY.zip`.

2. Create a Kaggle Notebook with GPU enabled and attach that Dataset.

3. Unzip in notebook:

```python

!unzip -q /kaggle/input//Project_Omega_FULL_DELIVERY.zip -d /kaggle/working/omega

```

4. Save notebook version via Kaggle UI using **Save Version**.

## Repository Layout

```text

.

+-- delivery_full/

+-- project_omega_m1_m2.py

+-- project_omega_m3.py

+-- project_omega_m4.py

+-- project_omega_m5.py

+-- project_omega_m6.py

+-- project_omega_m7.py

+-- project_omega_m8.py

+-- requirements.txt

```