A(n) APPO model trained on the dial-turn-v2 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 qgallouedec/sample-facotry-dial-turn-v2
Using the model
To run the model after download, use the enjoy
script corresponding to this environment:
python -m enjoy --algo=APPO --env=dial-turn-v2 --train_dir=./train_dir --experiment=sample-facotry-dial-turn-v2
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 train --algo=APPO --env=dial-turn-v2 --train_dir=./train_dir --experiment=sample-facotry-dial-turn-v2 --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.
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Evaluation results
- mean_reward on dial-turn-v2self-reported4604.42 +/- 42.83