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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

from google.colab import files
import io
import pandas as pd
import seaborn as sns

Plot parameters (edit as needed)



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# Make a bar plot for average results from the final 100 episodes (True),
# or make a learning curve plot (False)
bar_plot = False

# Compare different penalties using the best beta value for each penalty (True),
# or compare different beta values for the same penalty (False):
compare_penalties = False

# If compare_penalties is False, specify the penalty parameters:
dev_measure = 'rel_reach'
dev_fun = 'truncation'
value_discount = 0.99

# Environment name
env_name = 'box'

# Filename suffix
suffix = ''

Plot settings



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final_str = '_final' if bar_plot else ''
if compare_penalties:
  var = 'label'
  x_label = 'deviation_measure'
  legend_title = 'penalty'
  palette = sns.color_palette()
  filename = ('df_summary_penalties_' + env_name + final_str + suffix
              + '.csv')
else:
  var = 'beta'
  x_label = 'beta'
  legend_title = 'beta'
  palette = sns.cubehelix_palette()
  filename = ('df_summary_betas_' + env_name + '_' + dev_measure + '_' + dev_fun 
              + '_' + str(value_discount) + final_str + suffix + '.csv')

Load summary data output by results_summary.py



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uploaded = files.upload()



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df = pd.read_csv(io.BytesIO(uploaded[filename]))

Make bar plots



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plot = sns.catplot(data=df, col='baseline', x=var, y='performance_smooth',
                   kind='bar', height=4, aspect=1.3)
axes = plot.axes.flatten()
for ax in axes:
  title = ax.get_title().split()
  ax.set_title(title[2] + ' baseline')
  ax.set_ylabel('performance')
  ax.set_xlabel(x_label)

Make learning curve plots



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plot = sns.FacetGrid(df, col='baseline', size=5, aspect=1.3,
                     sharey=False, sharex=False)
plot.map_dataframe(sns.tsplot, time='episode', unit='seed', condition=var,
                   value='performance_smooth', n_boot=100, color=palette,
                   alpha=1.0, linewidth=1)
plot.add_legend(title=legend_title)
axes = plot.axes.flatten()
for ax in axes:
  title = ax.get_title().split()
  ax.set_title(title[2] + ' baseline')
  ax.set_ylabel('performance')
  ax.set_xlabel('episode')