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rl_course_vizdoom_doom_defend_the_center

Reinforcement Learning
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deep-reinforcement-learning
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rl_course_vizdoom_doom_defend_the_center
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---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
  results:
  - task:
      type: reinforcement-learning
      name: reinforcement-learning
    dataset:
      name: doom_defend_the_center
      type: doom_defend_the_center
    metrics:
    - type: mean_reward
      value: 16.20 +/- 1.54
      name: mean_reward
      verified: false
---

A(n) **APPO** model trained on the **doom_defend_the_center** environment.

This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/


## Downloading the model

After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r execbat/rl_course_vizdoom_doom_defend_the_center
```

    
## Using the model

To run the model after download, use the `enjoy` script corresponding to this environment:
```
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
```


You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
        
## Training with this model

To continue training with this model, use the `train` script corresponding to this environment:
```
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
```

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.