| | --- |
| | 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) |
| | ``` |
| |
|
