Plotting Results

stable_baselines.results_plotter.main()[source]

Example usage in jupyter-notebook

from stable_baselines import results_plotter
%matplotlib inline
results_plotter.plot_results(["./log"], 10e6, log_viewer.X_TIMESTEPS, "Breakout")

Here ./log is a directory containing the monitor.csv files

stable_baselines.results_plotter.plot_curves(xy_list, xaxis, title)[source]

plot the curves

Parameters:
  • xy_list – ([(np.ndarray, np.ndarray)]) the x and y coordinates to plot
  • xaxis – (str) the axis for the x and y output (can be X_TIMESTEPS=’timesteps’, X_EPISODES=’episodes’ or X_WALLTIME=’walltime_hrs’)
  • title – (str) the title of the plot
stable_baselines.results_plotter.plot_results(dirs, num_timesteps, xaxis, task_name)[source]

plot the results

Parameters:
  • dirs – ([str]) the save location of the results to plot
  • num_timesteps – (int or None) only plot the points below this value
  • xaxis – (str) the axis for the x and y output (can be X_TIMESTEPS=’timesteps’, X_EPISODES=’episodes’ or X_WALLTIME=’walltime_hrs’)
  • task_name – (str) the title of the task to plot
stable_baselines.results_plotter.rolling_window(array, window)[source]

apply a rolling window to a np.ndarray

Parameters:
  • array – (np.ndarray) the input Array
  • window – (int) length of the rolling window
Returns:

(np.ndarray) rolling window on the input array

stable_baselines.results_plotter.ts2xy(timesteps, xaxis)[source]

Decompose a timesteps variable to x ans ys

Parameters:
  • timesteps – (Pandas DataFrame) the input data
  • xaxis – (str) the axis for the x and y output (can be X_TIMESTEPS=’timesteps’, X_EPISODES=’episodes’ or X_WALLTIME=’walltime_hrs’)
Returns:

(np.ndarray, np.ndarray) the x and y output

stable_baselines.results_plotter.window_func(var_1, var_2, window, func)[source]

apply a function to the rolling window of 2 arrays

Parameters:
  • var_1 – (np.ndarray) variable 1
  • var_2 – (np.ndarray) variable 2
  • window – (int) length of the rolling window
  • func – (numpy function) function to apply on the rolling window on variable 2 (such as np.mean)
Returns:

(np.ndarray, np.ndarray) the rolling output with applied function