--- 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: mujoco_ant type: mujoco_ant metrics: - type: mean_reward value: 5761.72 +/- 1042.16 name: mean_reward verified: false --- A(n) **APPO** model trained on the **mujoco_ant** 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 execbat/sample-factory-mujoco-ant ``` ## Using the model To run the model after download, use the `enjoy` script corresponding to this environment: ``` python -m sf_examples.mujoco.enjoy_mujoco --algo=APPO --env=mujoco_ant --train_dir=./train_dir --experiment=sample-factory-mujoco-ant ``` 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 sf_examples.mujoco.train_mujoco --algo=APPO --env=mujoco_ant --train_dir=./train_dir --experiment=sample-factory-mujoco-ant --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.