| --- |
| tags: |
| - ML-Agents-Pyramids |
| - ppo |
| - deep-reinforcement-learning |
| - reinforcement-learning |
| - ml-agents |
| model-index: |
| - name: PPO |
| results: |
| - task: |
| type: reinforcement-learning |
| name: reinforcement-learning |
| dataset: |
| name: ML-Agents-Pyramids |
| type: ML-Agents-Pyramids |
| metrics: |
| - type: mean_reward |
| value: 5.10 +/- 0.85 |
| name: mean_reward |
| verified: false |
| --- |
| |
| # **PPO** Agent playing **ML-Agents-Pyramids** |
|
|
| This is a trained model of a **PPO** agent playing **ML-Agents-Pyramids** using Unity ML-Agents. |
|
|
| ## Usage |
|
|
| ```python |
| import torch |
| import numpy as np |
| |
| # Load the model (you'll need the network architecture) |
| checkpoint = torch.load("model.pt", map_location='cpu') |
| |
| # The model can be used with the Pyramids environment |
| # See the repository for complete usage instructions |
| ``` |
|
|
| ## Training Results |
|
|
| - **Mean reward**: 5.10 ± 0.85 |
| - **Average pyramids completed**: 5.0 per episode |
| - **Training episodes**: 3,000 |
| - **Target achievement**: ✅ SUCCESS (target: 1.75) |
|
|
| ## Algorithm Details |
|
|
| - **Algorithm**: Proximal Policy Optimization (PPO) |
| - **Environment**: ML-Agents-Pyramids |
| - **Task**: Multi-step pyramid completion with curiosity-driven exploration |
| - **Network**: Deep neural network with curiosity mechanism |
| - **Training Framework**: PyTorch |
|
|
| ## Task Description |
|
|
| The agent learns to: |
|
|
| 1. **Find and press buttons** to spawn pyramids |
| 2. **Navigate to pyramids** and knock them over |
| 3. **Collect gold bricks** from fallen pyramids |
| 4. **Repeat efficiently** to maximize score |
|
|
| This complex task requires: |
| - Exploration in sparse reward environment |
| - Multi-step planning and execution |
| - Spatial navigation and object interaction |
|
|
| ## Performance Milestones |
|
|
| - Episodes 0-500: Learning basic movement and object interaction |
| - Episodes 500-1500: Developing pyramid completion strategy |
| - Episodes 1500-3000: Optimizing efficiency and consistency |
|
|
| ## Training Environment |
|
|
| - **Environment**: ML-Agents-Pyramids |
| - **Framework**: Custom PyTorch implementation with ML-Agents compatibility |
| - **Training date**: 2025-09-05 |
| - **Course**: Hugging Face Deep RL Course Unit 5 |
|
|
| This model was trained as part of the [Hugging Face Deep RL Course](https://huggingface.co/learn/deep-rl-course). |
|
|
