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README.md
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license: mit
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tags:
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- reinforcement-learning
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- robotics
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- ppo
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- navigation
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---
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# JhonDOE Robot Models
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Trained models for the JhonDOE humanoid robot project.
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## Models
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### Navigate 2D
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- **Task**: 2D navigation with obstacle avoidance
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- **Algorithm**: PPO (Proximal Policy Optimization)
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- **
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##
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```bash
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cd execution
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python model_hub.py --mode download --repo DanielDel89/jhondoe-models --model navigate_2d_v01.zip
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```
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Or use in Python:
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```python
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from stable_baselines3 import PPO
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# Load model
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model = PPO.load("navigate_2d_v01.zip")
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# Use for inference
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obs = env.reset()
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action, _ = model.predict(obs)
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```
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## Project Links
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- **Training Space**: https://huggingface.co/spaces/DanielDel89/JhonDOE
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- **Documentation**: See training Space for full architecture details
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## Training Pipeline
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1. Configure training in Space (timesteps, parallel envs, etc.)
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2. Start training with GPU (T4 recommended)
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3. Model auto-uploads to this repository
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4. Download and evaluate locally
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## Model Versions
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Models are versioned with timestamps and tracked here. Latest versions are always recommended.
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---
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license: mit
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tags:
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- reinforcement-learning
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- robotics
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- ppo
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- navigation
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---
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# JhonDOE Robot Models
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Trained models for the JhonDOE humanoid robot project.
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## Models
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### Navigate 2D
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- **Task**: 2D navigation with obstacle avoidance
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- **Algorithm**: PPO (Proximal Policy Optimization)
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- **Status**: In training
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Models are uploaded automatically from the training Space.
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## Usage
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Download models:
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## Project
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- Training Space: https://huggingface.co/spaces/DanielDel89/JhonDOE
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- GitHub: [Add your repo URL]
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