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eolang/
DRL-vizdoome_health_gathering_supreme

Name: DRL-vizdoome_health_gathering_supreme
Availability: InStock

sample-factory

tensorboard

deep-reinforcement-learning

reinforcement-learning

model-index

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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 eolang/DRL-vizdoome_health_gathering_supreme

Using the model

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

python -m .workspace.stable-diffusion-webui.venv.lib.python3.10.site-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=DRL-vizdoome_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 .workspace.stable-diffusion-webui.venv.lib.python3.10.site-packages.ipykernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=DRL-vizdoome_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.