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license: mit
library_name: pytorch
tags:
- reinforcement-learning
- locomotion
- robotics
- g1
---
# g1-walk
PyTorch checkpoint for a G1 humanoid locomotion policy trained with
ADD (Adversarial Differential Discriminators).
## Checkpoint info
| Key | Value |
|-----|-------|
| Training iterations | `9,900` |
| Total environment samples | `5,190,975,488` |
| Number of parameters | `3,712,535` |
## Usage
```python
import torch
checkpoint = torch.load("model.pt", map_location="cpu")
state_dict = checkpoint["model"]
# Load into your agent:
# agent.load_state_dict(state_dict)
```
|