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Basic Usage

Usage examples

Use command line to train an agent using one of the existing integrations, e.g. Mujoco (might need to run pip install sample-factory[mujoco]):

python -m sf_examples.mujoco.train_mujoco --env=mujoco_ant --experiment=Ant --train_dir=./train_dir

Stop the experiment when the desired performance is reached and then evaluate the agent:

python -m sf_examples.mujoco.enjoy_mujoco --env=mujoco_ant --experiment=Ant --train_dir=./train_dir

Do the same in a pixel-based environment such as VizDoom (might need to run pip install sample-factory[vizdoom], please also see docs for VizDoom-specific instructions):

python -m sf_examples.vizdoom.train_vizdoom --env=doom_basic --experiment=DoomBasic --train_dir=./train_dir --num_workers=16 --num_envs_per_worker=10 --train_for_env_steps=1000000
python -m sf_examples.vizdoom.enjoy_vizdoom --env=doom_basic --experiment=DoomBasic --train_dir=./train_dir

Monitoring experiments

Monitor any running or completed experiment with Tensorboard:

tensorboard --logdir=./train_dir
(or see the docs for WandB integration).

Next steps

  • Read more about configuring experiments in the Configuration guide.
  • Follow the instructions in the Customizing guide to train an agent in your own environment.

Last update: May 9, 2023
Created: May 9, 2023