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library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
- PPO
- Unity
model-index:
- name: PPO-PyramidsTraining6
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pyramids
type: Unity-MLAgents-Env
metrics:
- type: mean_reward
value: 1.381
name: mean_reward
verified: false
- type: std_reward
value: 0.0
name: std_reward
verified: false
---
# 🏛️ **PPO Agent on Pyramids**
This repository contains a trained **Proximal Policy Optimization (PPO)** agent that plays the **Pyramids** environment using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
---
## 📊 Model Card
**Model Name:** `ppo-PyramidsTraining`
**Environment:** `Pyramids` (Unity ML-Agents)
**Algorithm:** PPO (Proximal Policy Optimization)
**Performance Metric:**
- Achieves stable performance in navigating and solving pyramid-based tasks
- Demonstrates convergence to an effective policy
---
## 🚀 Usage (with ML-Agents)
Documentation: [ML-Agents Toolkit Docs](https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/)
### Resume Training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Load and Run
```python
# Example: loading the trained PPO model
# (requires Unity ML-Agents setup)
model_id = "KraTUZen/ppo-PyramidsTraining"
# Select your .nn or .onnx file from the repo
```
---
## 🧠 Notes
- The agent is trained using **PPO**, a robust on-policy algorithm widely used in Unity ML-Agents.
- The environment involves **pyramid navigation and puzzle-solving**, requiring precision and strategy.
- The trained model is stored as `.nn` or `.onnx` files for direct Unity integration.
---
## 📂 Repository Structure
- `Pyramids.nn` / `Pyramids.onnx` → Trained PPO policy
- `README.md` → Documentation and usage guide
---
## ✅ Results
- The agent learns to navigate pyramid structures and solve tasks efficiently.
- Demonstrates stable training and effective policy convergence using PPO.
---
## 🔎 Environment Overview
- **Observation Space:** Continuous (agent position, pyramid state, environment features)
- **Action Space:** Continuous (movement, interaction)
- **Objective:** Solve pyramid-based tasks and maximize rewards
- **Reward:** Positive reward for successful task completion, penalties for failures
---
## 📚 Learning Highlights
- **Algorithm:** PPO (Proximal Policy Optimization)
- **Update Rule:** Clipped surrogate objective to ensure stable updates
- **Strengths:** Robust, stable, widely used in Unity ML-Agents
- **Limitations:** Requires careful tuning of hyperparameters (clip ratio, learning rate, batch size)
---
## 🎮 Watch Your Agent Play
You can watch your agent **directly in your browser**:
1. Visit [Unity ML-Agents on Hugging Face](https://huggingface.co/unity)
2. Find your model ID: `KraTUZen/ppo-PyramidsTraining`
3. Select your `.nn` or `.onnx` file
4. Click **Watch the agent play 👀**
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