Using Pretrained Models#

This tutorial shows how to use pretrained model checkpoints.

Jupyter Notebook: Working with Pretrained Policies

The rest of this tutorial shows how to use utility scripts to load and rollout a trained policy. If you wish to do so via an interactive notebook, please refer to the jupyter notebook at examples/notebooks/run_policy.ipynb. The notebook tutorial shows how to download a checkpoint from the model zoo, load the checkpoint in pytorch, and rollout the policy.

Evaluating Trained Policies#

Saved policy checkpoints in the models directory can be evaluated using the script:

# 50 rollouts with max horizon 400 and render agentview and wrist camera images to video
$ python --agent /path/to/model.pth --n_rollouts 50 --horizon 400 --seed 0 --video_path /path/to/output.mp4 --camera_names agentview robot0_eye_in_hand 

# Write rollouts to a new dataset hdf5
python --agent /path/to/model.pth --n_rollouts 50 --horizon 400 --seed 0 --dataset_path /path/to/output.hdf5 --dataset_obs

# Write rollouts without explicit observations to hdf5
python --agent /path/to/model.pth --n_rollouts 50 --horizon 400 --seed 0 --dataset_path /path/to/output.hdf5

In the last case, the observations can be (later) extracted later using the script (see here).