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Upload README.md with huggingface_hub

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@@ -15,7 +15,7 @@ model-index:
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  type: doom_health_gathering_supreme
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  metrics:
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  - type: mean_reward
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- value: 8.20 +/- 4.67
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  name: mean_reward
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  verified: false
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  ---
@@ -38,19 +38,19 @@ python -m sample_factory.huggingface.load_from_hub -r sheryliza/rl_course_vizdoo
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  To run the model after download, use the `enjoy` script corresponding to this environment:
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  ```
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- python -m <path.to.enjoy.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
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  ```
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  You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
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  See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
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-
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  ## Training with this model
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  To continue training with this model, use the `train` script corresponding to this environment:
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  ```
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- python -m <path.to.train.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
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  ```
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  Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
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-
 
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  type: doom_health_gathering_supreme
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  metrics:
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  - type: mean_reward
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+ value: 9.62 +/- 5.68
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  name: mean_reward
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  verified: false
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  ---
 
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  To run the model after download, use the `enjoy` script corresponding to this environment:
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  ```
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+ python -m .usr.local.lib.python3.8.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
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  ```
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  You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
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  See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
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+
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  ## Training with this model
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  To continue training with this model, use the `train` script corresponding to this environment:
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  ```
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+ python -m .usr.local.lib.python3.8.dist-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
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  ```
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  Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
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+