
A(n) **APPO** model trained on the **doom_health_gathering_supreme** 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 kraken2404/rl_course_vizdoom_health_gathering_supreme
```

    
## Using the model

To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m <path.to.enjoy.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```


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 .Users.brijesh.modasara.miniconda3.envs.rl_unit8_p310.lib.python3.10.site-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
```

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.
    
{"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_health_gathering_supreme", "type": "doom_health_gathering_supreme"}, "metrics": [{"type": "mean_reward", "value": "9.52 +/- 6.40", "name": "mean_reward", "verified": false}]}]}]}
sample-factory tensorboard deep-reinforcement-learning reinforcement-learning model-index region:us
TAGS
#sample-factory #tensorboard #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 


A(n) APPO model trained on the doom_health_gathering_supreme environment.

This model was trained using Sample-Factory 2.0: URL
Documentation for how to use Sample-Factory can be found at URL


## Downloading the model

After installing Sample-Factory, download the model with:


    
## Using the model

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



You can also upload models to the Hugging Face Hub using the same script with the '--push_to_hub' flag.
See URL for more details
    
## Training with this model

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


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.
    
## Downloading the model

After installing Sample-Factory, download the model with: ## Using the model

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



You can also upload models to the Hugging Face Hub using the same script with the '--push_to_hub' flag.
See URL for more details ## Training with this model

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


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.
TAGS
#sample-factory #tensorboard #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 
 ## Downloading the model

After installing Sample-Factory, download the model with: ## Using the model

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



You can also upload models to the Hugging Face Hub using the same script with the '--push_to_hub' flag.
See URL for more details ## Training with this model

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


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.
TAGS
#sample-factory #tensorboard #deep-reinforcement-learning #reinforcement-learning #model-index #region-us 
## Downloading the model

After installing Sample-Factory, download the model with:## Using the model

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



You can also upload models to the Hugging Face Hub using the same script with the '--push_to_hub' flag.
See URL for more details## Training with this model

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


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.