Add todo for train
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@ -152,8 +152,9 @@ See `python lerobot/scripts/eval.py --help` for more instructions.
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Check out [example 3](./examples/3_train_policy.py) to see how you can start training a model on a dataset, which will be automatically downloaded if needed.
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In general, you can use our training script to easily train any policy in any environment:
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In general, you can use our training script to easily train any policy on its environment:
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```bash
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# TODO(aliberts): not working
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python lerobot/scripts/train.py \
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env=aloha \
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task=sim_insertion \
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@ -18,7 +18,7 @@ from lerobot.common.utils.utils import init_hydra_config
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output_directory = Path("outputs/train/example_pusht_diffusion")
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os.makedirs(output_directory, exist_ok=True)
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# Number of offline training steps (we'll only do offline training for this example.
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# Number of offline training steps (we'll only do offline training for this example.)
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# Adjust as you prefer. 5000 steps are needed to get something worth evaluating.
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training_steps = 5000
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device = torch.device("cuda")
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