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: push-wall-v2
type: push-wall-v2
metrics:
- type: mean_reward
value: 4317.91 +/- 1202.36
name: mean_reward
verified: false
A(n) APPO model trained on the push-wall-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-factory-push-wall-v2
Using the model
To run the model after download, use the enjoy script corresponding to this environment:
python -m enjoy --algo=APPO --env=push-wall-v2 --train_dir=./train_dir --experiment=sample-factory-push-wall-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=push-wall-v2 --train_dir=./train_dir --experiment=sample-factory-push-wall-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.