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elliemci/
doom_health_gathering_supreme

Name: doom_health_gathering_supreme
Availability: InStock

sample-factory

tensorboard

deep-reinforcement-learning

reinforcement-learning

model-index

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An Accelerated Proximal Policy Optimization model trained on the VizDoom Health Gathering Supreme environment. The agent's goal is to survive as long as possible the hazardous floor of acidic substance by picking up medkits.

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 elliemci/doom_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=doom_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 <path.to.train.module> --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=doom_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.