eval

name	eval
description	Evaluate a trained checkpoint with visualization
disable-model-invocation	true
allowed-tools	Bash, Read, Glob
argument-hint	["path-or-logdir"]

/eval — Evaluate a Trained Checkpoint

Parse the user's request from $ARGUMENTS and run evaluation.

Command Template

uv run python run_experiment.py eval --path <PATH> [OPTIONS...]

Path Resolution

The user may provide:

• A .pt file: Use directly (--path /tmp/.../actor_999.pt)
• A run directory: Contains actor*.pt files (--path /tmp/.../26-03-07-00-26-36_cartpole/)
• A logdir: Contains timestamped run subdirectories (--logdir /tmp/training_runs)

If no path is given, check /tmp/training_runs for the most recent run.

Use Glob to verify the path exists and resolve it before running.

Options

Flag	Default	Description
--ep-len	10	Episode length in seconds
--seed	None	Random seed for reproducible eval
--out-dir	None	Directory to save videos

Instructions

1. Resolve the model path from the user's input. If ambiguous, list available checkpoints and ask.
2. Show the user which checkpoint will be evaluated (full path).
3. Run the eval command. This opens an interactive MuJoCo viewer window — it is NOT a background job.
4. Report the results when done.