forked from tangger/lerobot
Fix typos (#1070)
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@@ -13,7 +13,7 @@
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# limitations under the License.
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"""
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This scripts demonstrates how to evaluate a pretrained policy from the HuggingFace Hub or from your local
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This script demonstrates how to evaluate a pretrained policy from the HuggingFace Hub or from your local
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training outputs directory. In the latter case, you might want to run examples/3_train_policy.py first.
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It requires the installation of the 'gym_pusht' simulation environment. Install it by running:
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@@ -119,7 +119,7 @@ while not done:
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rewards.append(reward)
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frames.append(env.render())
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# The rollout is considered done when the success state is reach (i.e. terminated is True),
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# The rollout is considered done when the success state is reached (i.e. terminated is True),
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# or the maximum number of iterations is reached (i.e. truncated is True)
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done = terminated | truncated | done
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step += 1
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