File size: 952 Bytes
ecd1c2e |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 |
# Huggy - Trained Agent
**Author:** Vishand03
**Model Type:** Reinforcement Learning (PPO)
**Environment:** Custom Huggy Environment (ML-Agents)
**Framework:** ML-Agents + PyTorch
---
## Description
This model is a trained Huggy agent using the PPO algorithm.
It learns to navigate and complete tasks in the Huggy environment.
---
## 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
---
## 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...
|