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README.md
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---
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library_name: sample-factory
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tags:
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- deep-reinforcement-learning
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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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name: reinforcement-learning
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dataset:
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name: doom_defend_the_center
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type: doom_defend_the_center
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metrics:
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- type: mean_reward
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value: 16.20 +/- 1.54
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name: mean_reward
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verified: false
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---
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A(n) **APPO** model trained on the **doom_defend_the_center** 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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## Downloading the model
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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 execbat/rl_course_vizdoom_doom_defend_the_center
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```
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## Using the model
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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 .home.evgenii.anaconda3.envs.hf_unit_8_doom.lib.python3.9.site-packages.ipykernel_launcher --algo=APPO --env=doom_defend_the_center --train_dir=./train_dir --experiment=rl_course_vizdoom_doom_defend_the_center
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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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## 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 .home.evgenii.anaconda3.envs.hf_unit_8_doom.lib.python3.9.site-packages.ipykernel_launcher --algo=APPO --env=doom_defend_the_center --train_dir=./train_dir --experiment=rl_course_vizdoom_doom_defend_the_center --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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