https://github.com/omarelfiki/unity-ml-drl-data
Collecting data on agents training/acting in 3D simulations and analyzing it with ML
https://github.com/omarelfiki/unity-ml-drl-data
3d-models csharp machine-learning ml-agents python unity
Last synced: about 2 months ago
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Collecting data on agents training/acting in 3D simulations and analyzing it with ML
- Host: GitHub
- URL: https://github.com/omarelfiki/unity-ml-drl-data
- Owner: omarelfiki
- Created: 2025-09-03T10:28:35.000Z (10 months ago)
- Default Branch: main
- Last Pushed: 2025-09-03T12:21:58.000Z (10 months ago)
- Last Synced: 2025-09-03T12:27:51.459Z (10 months ago)
- Topics: 3d-models, csharp, machine-learning, ml-agents, python, unity
- Language: Mathematica
- Homepage:
- Size: 4.88 KB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# unity-ml-drl-data
### Group 6 - P2-1: Artificial Intelligence & Machine Learning
unity-ml-drl-data is a GitHub repository for experimenting with Deep Reinforcement Learning (DRL) using Unity and ML-Agents.
This project uses simulated 3D environments to study and train agents with DRL algorithms, while logging performance and behavioral data using TensorBoard for analysis using Machine Learning Techniques to make predictions on training results.
More information can be found in the [`docs/`](https://www.github.com/omarelfiki/unity-ml-drl-data/tree/main/docs) directory.
### Repository Structure
```
unity-ml-drl-data/
│
├── unity/ # Unity project files (scenes, agents, environment scripts)
├── training/ # Python training scripts, configs, and utilities
├── data/ # Collected data and schema definitions
├── docs/ # Documentation, research notes, and reports
├── models/ # Trained prediction models and ML evaluation scripts
├── README.md # This file
├── CONTRIBUTING.md # Guidelines for making contributions
└── SETUP.md # In-depth installation steps
```
### Installation
Clone Repository and setup Python Environment
```
git clone https://github.com/omarelfiki/unity-ml-drl-data.git
cd unity-ml-drl-data/training
python setup_env.py
```
> Unity 2023.2.12f1 is a requirement for this project. Unity versions can be downloaded via Unity Hub.
See [```SETUP.md```](https://www.github.com/omarelfiki/unity-ml-drl-data/tree/main/SETUP.md)for in-depth installation steps.
### Training
from ```training/```:
```
python -m scripts.train [-h] --config --run-id [--num-steps ] [--headless ]
```
> Example Usage for training script. Results will be appended to shared dataset. Local results are directed to gitignore.
See [```training/README.md```](https://www.github.com/omarelfiki/unity-ml-drl-data/tree/main/training/README.md) for more information on the `training` package.
### Modeling
> See Models CLI on all collected environments and datasets through GitHub actions here: https://github.com/omarelfiki/unity-ml-drl-data/actions/workflows/run_models.yml. Artifacts can be downloaded. Contact repository administrators for run access.
from ```models/```:
```
python -m scripts.run [-h] [--test_size ] [--seed ] [--thresh ] [--env ] [--models-dir ] [--data-csv, ]
```
> Example Usage for modelling script. Results will be available under ```models/experiments/```
>
> Once completed, the results will be available in the artifacts tab of the workflow run as a zip file containing the collected models and results.
See [```models/README.md```](https://www.github.com/omarelfiki/unity-ml-drl-data/tree/main/modles/README.md) for more information on the `models` package.
### Results
Results are obtained in a shared dataset updated each training run with new metrics. Upon being pushed to the repository, data validation checks are executed automatically by GitHub workflows. A summary report is also available providing average dataset values and plots from TensorBoard data for visualization.
See [```data/```](https://www.github.com/omarelfiki/unity-ml-drl-data/tree/main/data) for results and documentation.
## Attributions
This project includes the official **Unity ML-Agents Examples and corresponding training configuration files**, sourced from the [Unity ML-Agents GitHub Repository](https://github.com/Unity-Technologies/ml-agents).
All rights to these examples belong to Unity Technologies. We claim no ownership over them.
#### Dependencies
1. **Unity side**: ML-Agents 2.0.1 (installed automatically via Unity Package Manager)
2. **Python side**: Dennis Soemers’ [ML-Agents fork](https://www.github.com/dennissoemers/ml-agents)
### License Notice
The Unity ML-Agents Examples included here remain under their original [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0), as provided by Unity Technologies. All other code and assets created for this repository are licensed under the terms specified in this project’s LICENSE file.