| --- |
| 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 👀** |
|
|
|
|
