Plotting Results

SLC ships with a simple plotting utility that can be used to plot diagnostics from experiments. You can run it with:

python -m stable_learning_control.run plot [path/to/output_directory ...] [-h] [--legend [LEGEND [LEGEND ...]]]
    [--xaxis XAXIS] [--value [VALUE [VALUE ...]]] [--count] [--smooth SMOOTH]
    [--select [SELECT [SELECT ...]]] [--exclude [EXCLUDE [EXCLUDE ...]]] [--est EST]

See also

For more information on this utility, see the plot utility documentation or code the API reference.

../_images/example_lac_performance_plot.svg

Example plot that displays the performance of the LAC algorithm.

Tip

The SLC package also supports TensorBoard and Weights & Biases logging. See Loggers for more information. This allows you to inspect your experiments’ results during training and compare the performance of different algorithms more interactively.