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# Copyright (c) 2019-2020, RTE (https://www.rte-france.com) | ||
# See AUTHORS.txt | ||
# This Source Code Form is subject to the terms of the Mozilla Public License, version 2.0. | ||
# If a copy of the Mozilla Public License, version 2.0 was not distributed with this file, | ||
# you can obtain one at http://mozilla.org/MPL/2.0/. | ||
# SPDX-License-Identifier: MPL-2.0 | ||
# This file is part of Grid2Op, Grid2Op a testbed platform to model sequential decision making in power systems. | ||
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import warnings | ||
import unittest | ||
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from grid2op.tests.helper_path_test import * | ||
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PATH_ADN_CHRONICS_FOLDER = os.path.abspath( | ||
os.path.join(PATH_CHRONICS, "test_multi_chronics") | ||
) | ||
PATH_PREVIOUS_RUNNER = os.path.join(data_test_dir, "runner_data") | ||
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import grid2op | ||
from grid2op.Runner import Runner | ||
from grid2op.dtypes import dt_float | ||
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warnings.simplefilter("error") | ||
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class TestRunner(HelperTests, unittest.TestCase): | ||
def setUp(self): | ||
super().setUp() | ||
self.init_grid_path = os.path.join(PATH_DATA_TEST_PP, "test_case14.json") | ||
self.path_chron = PATH_ADN_CHRONICS_FOLDER | ||
self.parameters_path = None | ||
self.max_iter = 10 | ||
self.real_reward = dt_float(7748.425 / 12.) | ||
self.real_reward_li = [self.real_reward, dt_float(7786.8955 / 12.)] # 7786.89599609375 | ||
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self.all_real_rewards = [ | ||
dt_float(el / 12.) | ||
for el in [ | ||
761.3295, | ||
768.10144, | ||
770.2673, | ||
767.767, | ||
768.69, | ||
768.71246, | ||
779.1029, | ||
783.2737, | ||
788.7833, | ||
792.39764, | ||
] | ||
] | ||
with warnings.catch_warnings(): | ||
warnings.filterwarnings("ignore") | ||
self.env = grid2op.make("l2rpn_case14_sandbox", test=True, _add_to_name=type(self).__name__) | ||
self.runner = Runner(**self.env.get_params_for_runner()) | ||
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def test_one_episode(self): | ||
with warnings.catch_warnings(): | ||
warnings.filterwarnings("ignore") | ||
_, cum_reward, timestep, max_ts = self.runner.run_one_episode( | ||
max_iter=self.max_iter | ||
) | ||
assert int(timestep) == self.max_iter | ||
assert np.abs(cum_reward - self.real_reward) <= self.tol_one, f"{cum_reward} != {self.real_reward}" | ||
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def test_one_episode_detailed(self): | ||
with warnings.catch_warnings(): | ||
warnings.filterwarnings("ignore") | ||
_, cum_reward, timestep, max_ts, episode_data = self.runner.run_one_episode( | ||
max_iter=self.max_iter, detailed_output=True | ||
) | ||
assert int(timestep) == self.max_iter | ||
assert np.abs(cum_reward - self.real_reward) <= self.tol_one | ||
for j in range(len(self.all_real_rewards)): | ||
assert ( | ||
np.abs(episode_data.rewards[j] - self.all_real_rewards[j]) | ||
<= self.tol_one | ||
), f"{episode_data.rewards[j]} != {self.all_real_rewards[j]}" | ||
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def test_2episode(self): | ||
with warnings.catch_warnings(): | ||
warnings.filterwarnings("ignore") | ||
res = self.runner._run_sequential(nb_episode=2, max_iter=self.max_iter) | ||
assert len(res) == 2 | ||
for i, (stuff, _, cum_reward, timestep, total_ts) in enumerate(res): | ||
assert int(timestep) == self.max_iter | ||
assert np.abs(cum_reward - self.real_reward_li[i]) <= self.tol_one, f"for iter {i}: {cum_reward} != {self.real_reward_li[i]}" | ||
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def test_init_from_env(self): | ||
with warnings.catch_warnings(): | ||
warnings.filterwarnings("ignore") | ||
with grid2op.make("rte_case14_test", test=True, _add_to_name=type(self).__name__) as env: | ||
runner = Runner(**env.get_params_for_runner()) | ||
res = runner.run(nb_episode=1, max_iter=self.max_iter) | ||
for i, _, cum_reward, timestep, total_ts in res: | ||
assert int(timestep) == self.max_iter, f"{timestep} != {self.max_iter}" | ||
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def test_seed_seq(self): | ||
with warnings.catch_warnings(): | ||
warnings.filterwarnings("ignore") | ||
with grid2op.make("rte_case14_test", test=True, _add_to_name=type(self).__name__) as env: | ||
runner = Runner(**env.get_params_for_runner()) | ||
res = runner.run( | ||
nb_episode=1, max_iter=self.max_iter, env_seeds=[1], agent_seeds=[2] | ||
) | ||
for i, _, cum_reward, timestep, total_ts in res: | ||
assert int(timestep) == self.max_iter, f"{timestep} != {self.max_iter}" | ||
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def test_seed_par(self): | ||
with warnings.catch_warnings(): | ||
warnings.filterwarnings("ignore") | ||
with grid2op.make("rte_case14_test", test=True, _add_to_name=type(self).__name__) as env: | ||
runner = Runner(**env.get_params_for_runner()) | ||
res = runner.run( | ||
nb_episode=2, | ||
nb_process=2, | ||
max_iter=self.max_iter, | ||
env_seeds=[1, 2], | ||
agent_seeds=[3, 4], | ||
) | ||
for i, _, cum_reward, timestep, total_ts in res: | ||
assert int(timestep) == self.max_iter | ||
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if __name__ == "__main__": | ||
unittest.main() |
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