|
|
--- |
|
|
library_name: ml-agents |
|
|
tags: |
|
|
- Huggy |
|
|
- deep-reinforcement-learning |
|
|
- reinforcement-learning |
|
|
- ML-Agents-Huggy |
|
|
--- |
|
|
|
|
|
# PPO Huggy Training 🐻 |
|
|
|
|
|
**My Github Repo:** [PardhuSreeRushiVarma20060119/HuggingFace-Training/ppo-HuggyTraining](https://github.com/PardhuSreeRushiVarma20060119/HuggingFace-Training/ppo-HuggyTraining) |
|
|
|
|
|
This repository contains a **trained Proximal Policy Optimization (PPO) agent** playing the **Huggy environment**, built using the **Unity ML-Agents library** and integrated with the Hugging Face Hub. |
|
|
The goal of this project was to explore reinforcement learning (RL) with Unity environments and make the trained agent accessible and interactive through Hugging Face. |
|
|
|
|
|
--- |
|
|
## 🚀 Usage |
|
|
If you’re new to ML-Agents, check out the official [ML-Agents Documentation](https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/) for setup, installation, and training details. |
|
|
You can also dive into Hugging Face’s **[Deep RL Course](https://huggingface.co/learn/deep-rl-course/)** for step-by-step guidance on how to train agents and upload them to the Hub. |
|
|
|
|
|
--- |
|
|
## 🎮 Play with your Huggy 🐕 |
|
|
|
|
|
This is the fun part! You can **interactively play with your trained Huggy agent** directly in your browser: |
|
|
👉 Open the Huggy game here: [Huggy on Hugging Face Spaces](https://huggingface.co/spaces/ThomasSimonini/Huggy) |
|
|
|
|
|
1. Click **“Play with my Huggy model”** |
|
|
2. In **Step 1**, enter your exact Hugging Face username (case-sensitive). Example: `VarmaHF` |
|
|
3. In **Step 2**, select the repository: `ppo-HuggyTraining` |
|
|
4. In **Step 3**, choose the model checkpoint you want to replay. |
|
|
|
|
|
💡 During training, multiple model checkpoints were saved (e.g., every 200,000 timesteps). |
|
|
You can try different versions to observe how Huggy improves over time. |
|
|
For example, the most recent model file is: **`Huggy.onnx`** |
|
|
|
|
|
--- |
|
|
|
|
|
## ⚙️ Training Setup |
|
|
- **Environment**: Huggy (Unity ML-Agents) |
|
|
- **Algorithm**: PPO (Proximal Policy Optimization) |
|
|
- **Frameworks**: Unity ML-Agents + Hugging Face Hub |
|
|
- **Integration**: Model packaged and uploaded to Hugging Face for sharing and deployment |
|
|
|
|
|
--- |
|
|
|
|
|
## 📊 Results |
|
|
The PPO agent was trained successfully and learned to play the Huggy environment. |
|
|
Thanks to the Hugging Face integration, you can: |
|
|
|
|
|
- Preview the trained agent |
|
|
- Replay and test different checkpoints |
|
|
- Interactively compare performance improvements over training |
|
|
|
|
|
--- |
|
|
|
|
|
## 📚 References |
|
|
- [Unity ML-Agents Toolkit](https://github.com/Unity-Technologies/ml-agents) |
|
|
- [Hugging Face Deep RL Course](https://huggingface.co/learn/deep-rl-course/) |
|
|
- [Hugging Face Spaces - Huggy Game](https://huggingface.co/spaces/ThomasSimonini/Huggy) |
|
|
|
|
|
--- |
|
|
|
|
|
✨ **Enjoy training, exploring, and playing with Huggy!** 🐻 |
