Welcome to rscope!

A light-weight package to collect and interactively visualize trajectories while training Mujoco Playground environments. Rscope can visualize both local and remote (potentially headless) training runs.

Installation

Important

• Requires Python 3.10 or later.

pip install rscope

---

Usage

Important

Mac users must run mjpython instead of python, ex. mjpython -m rscope

Local training runs

To visualize locally stored rollouts:

python -m rscope

Remote training runs

Below, update user@remote_host, for example alice@168.42.4.8.

First, set up password-free key-based SSH connection with the remote device:

ssh-keygen -t ed25519 -f ~/.ssh/rsync_key -N ""
ssh-copy-id -i ~/.ssh/rsync_key.pub user@remote_host

If this worked, you should be able to ssh in without using a password:

ssh -i ~/.ssh/rsync_key user@remote_host
echo hello
exit

To visualize rollouts stored on a remote server via SSH:

python -m rscope --ssh_to user@remote_host[:port] --ssh_key ~/.ssh/rsync_key --polling_interval 5 # port defaults to 22

---

Features

1. Most features from Mujoco viewer
2. Browse through trajectories. Use left/right arrow keys to switch through parallel environments and up/down for recent/past trajectories.
3. Live Plotting. Use SHIFT+M to plot trajectory rewards and the contents of state.metrics, up to the first 11 keys.
4. Pixel Observations. Use SHIFT+O to overlay pixel observations if available. To use this feature, the observation must be a dict and the pixel keys must be prefixed with pixels/.

---

Sharp bits

Some background on how rscope works: between policy updates, rscope unrolls multiple trajectories in parallel then visualizes them on CPU. While this is simpler to implement and less expensive than tracing training runs like in IsaacLab, this and other implementation details lead to some unexpected gotchas:

• Typically, stochastic policies are used for evaluating training progress while determinsitic ones are deployed. While you can use rscope on stochastic policies to get a feel for the agent's training exploration, we recommend deterministic evals.
• Renders incorrectly for domain-randomized training because the loaded assets are from the nominal model definition.
• Plots only the first 14 keys in the metrics without filtering for shaping rewards.
• Visualizes only the first 14 pixel observations.
• Cannot capture curriculum progression during training, as curriculums depend on state.info, which is reset at the start of an evaluator run.
• Currently supports only PPO-based training.

---

Contribution Guidelines:

Please run the following before making a PR:

pip install -e ".[dev]"
pre-commit install
pre-commit run --all-files