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Update README.md
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README.md
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@@ -5,7 +5,7 @@ tags:
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- reinforcement-learning
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- sample-factory
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model-index:
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-
- name:
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results:
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- task:
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type: reinforcement-learning
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verified: false
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---
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A(n) **
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This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
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Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
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After installing Sample-Factory, download the model with:
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```
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python -m sample_factory.huggingface.load_from_hub -r MattStammers/
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```
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@@ -38,7 +38,7 @@ python -m sample_factory.huggingface.load_from_hub -r MattStammers/atd3-mujoco-s
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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 sf_examples.mujoco.enjoy_mujoco --algo=
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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 sf_examples.mujoco.train_mujoco --algo=
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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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- reinforcement-learning
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- sample-factory
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model-index:
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- name: APPO
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results:
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- task:
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type: reinforcement-learning
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verified: false
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---
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A(n) **APPO** model trained on the **mujoco_swimmer** environment.
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This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
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Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
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After installing Sample-Factory, download the model with:
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```
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python -m sample_factory.huggingface.load_from_hub -r MattStammers/appo-mujoco-swimmer
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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 sf_examples.mujoco.enjoy_mujoco --algo=APPO --env=mujoco_swimmer --train_dir=./train_dir --experiment=appo-mujoco-swimmer
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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 sf_examples.mujoco.train_mujoco --algo=APPO --env=mujoco_swimmer --train_dir=./train_dir --experiment=appo-mujoco-swimmer --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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