labicquette
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Upload README.md with huggingface_hub
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README.md
CHANGED
@@ -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:
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name: mean_reward
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verified: false
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---
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@@ -38,7 +38,7 @@ python -m sample_factory.huggingface.load_from_hub -r labicquette/rl_course_vizd
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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 .
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```
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@@ -49,7 +49,7 @@ See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
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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 .
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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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type: doom_health_gathering_supreme
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metrics:
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- type: mean_reward
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value: 9.21 +/- 3.73
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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.python3.10.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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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.python3.10.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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