Fix multiprocessing error on Windows by adding main guard

examples/3_train_policy.py

@@ -12,6 +12,8 @@ from lerobot.common.datasets.lerobot_dataset import LeRobotDataset
 from lerobot.common.policies.diffusion.configuration_diffusion import DiffusionConfig
 from lerobot.common.policies.diffusion.modeling_diffusion import DiffusionPolicy
 
+
+def main():
     # Create a directory to store the training checkpoint.
     output_directory = Path("outputs/train/example_pusht_diffusion")
     output_directory.mkdir(parents=True, exist_ok=True)
@@ -49,7 +51,7 @@ optimizer = torch.optim.Adam(policy.parameters(), lr=1e-4)
     # Create dataloader for offline training.
     dataloader = torch.utils.data.DataLoader(
         dataset,
-    num_workers=4,
+        num_workers=0,
         batch_size=64,
         shuffle=True,
         pin_memory=device != torch.device("cpu"),
@@ -77,3 +79,7 @@ while not done:
 
     # Save a policy checkpoint.
     policy.save_pretrained(output_directory)
+
+
+if __name__ == "__main__":
+    main()