# Huggy - Trained Agent

**Author:** Vishand03  
**Model Type:** Reinforcement Learning (PPO)  
**Environment:** Custom Huggy Environment (ML-Agents)  
**Framework:** ML-Agents + PyTorch  

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## Description
This model is a trained Huggy agent using the PPO algorithm.  
It learns to navigate and complete tasks in the Huggy environment.  

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## Training Details
- **Trainer:** PPO  
- **Steps:** ~800,000 (can be resumed)  
- **Reward:** ~3.9 mean reward at the last checkpoint  
- **Hyperparameters:**
  - Batch size: 4096  
  - Buffer size: 40960  
  - Learning rate: 0.0001  
  - Gamma: 0.995  
  - Lambda: 0.95  

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## Usage
```python
from mlagents_envs.environment import UnityEnvironment
from mlagents_envs.base_env import ActionTuple
import onnxruntime as ort

env = UnityEnvironment(file_name="Huggy.x86_64", no_graphics=True)
# Load model
session = ort.InferenceSession("Huggy-799913.onnx")
# Continue with your inference pipeline...