episode.rst

Episode

This page is organized as follow:

Table of Contents

• Objectives
• Detailed Documentation by class

Objectives

Grid2op defines some special function that help with restoring agent that has run during some episode that has been saved by the runner.

Here are some basic usage.

First you run an episode:

import grid2op
from grid2op.Runner import Runner

# I create an environment
env = grid2op.make("rte_case5_example", test=True)

# I create the runner
runner = Runner(**env.get_params_for_runner())
path_save = "/I/SAVED/RESULTS/THERE"

# I start the runner and save the results in "/I/SAVED/RESULTS/THERE"
# I start the evaluation on 2 different episode
res = runner.run(path_save=path_save, nb_episode=2)

Second you can reload the data (here to plot the different productions active values):

import grid2op
from grid2op.Episode import EpisodeData

# I study only the first episode saved, because... why not
path_saved = "/I/SAVED/RESULTS/THERE"  # same path as before

li_episode = EpisodeData.list_episode(path_saved)
full_path, episode_studied = li_episode[0]
this_episode = EpisodeData.from_disk(path_agent, episode_studied)

# now the episode is loaded, and you can easily iterate through the observation, the actions etc.
for act in this_episode.actions:
    print(act)

for i, obs in enumerate(this_episode.observations):
    print("At step {} the active productions were {}".format(i, obs.prod_p))

# etc. etc.

Detailed Documentation by class

.. automodule:: grid2op.Episode
    :members:
    :autosummary: