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Update vector_envs_tutorial.py (#390)
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@@ -454,7 +454,7 @@ for sample_phase in tqdm(range(n_updates)):
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# perform the action A_{t} in the environment to get S_{t+1} and R_{t+1}
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states, rewards, terminated, truncated, infos = envs_wrapper.step(
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actions.numpy()
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actions.cpu().numpy()
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)
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ep_value_preds[step] = torch.squeeze(state_value_preds)
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