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| 1 | +# Copyright (c) 2019-2020, RTE (https://www.rte-france.com) |
| 2 | +# See AUTHORS.txt |
| 3 | +# This Source Code Form is subject to the terms of the Mozilla Public License, version 2.0. |
| 4 | +# If a copy of the Mozilla Public License, version 2.0 was not distributed with this file, |
| 5 | +# you can obtain one at http://mozilla.org/MPL/2.0/. |
| 6 | +# SPDX-License-Identifier: MPL-2.0 |
| 7 | +# This file is part of Grid2Op, Grid2Op a testbed platform to model sequential decision making in power systems. |
| 8 | + |
| 9 | +import warnings |
| 10 | +import unittest |
| 11 | + |
| 12 | +from grid2op.tests.helper_path_test import * |
| 13 | + |
| 14 | +PATH_ADN_CHRONICS_FOLDER = os.path.abspath( |
| 15 | + os.path.join(PATH_CHRONICS, "test_multi_chronics") |
| 16 | +) |
| 17 | +PATH_PREVIOUS_RUNNER = os.path.join(data_test_dir, "runner_data") |
| 18 | + |
| 19 | +import grid2op |
| 20 | +from grid2op.Runner import Runner |
| 21 | +from grid2op.dtypes import dt_float |
| 22 | + |
| 23 | +warnings.simplefilter("error") |
| 24 | + |
| 25 | + |
| 26 | +class TestRunner(HelperTests, unittest.TestCase): |
| 27 | + def setUp(self): |
| 28 | + super().setUp() |
| 29 | + self.init_grid_path = os.path.join(PATH_DATA_TEST_PP, "test_case14.json") |
| 30 | + self.path_chron = PATH_ADN_CHRONICS_FOLDER |
| 31 | + self.parameters_path = None |
| 32 | + self.max_iter = 10 |
| 33 | + self.real_reward = dt_float(7748.425 / 12.) |
| 34 | + self.real_reward_li = [self.real_reward, dt_float(7786.8955 / 12.)] # 7786.89599609375 |
| 35 | + |
| 36 | + self.all_real_rewards = [ |
| 37 | + dt_float(el / 12.) |
| 38 | + for el in [ |
| 39 | + 761.3295, |
| 40 | + 768.10144, |
| 41 | + 770.2673, |
| 42 | + 767.767, |
| 43 | + 768.69, |
| 44 | + 768.71246, |
| 45 | + 779.1029, |
| 46 | + 783.2737, |
| 47 | + 788.7833, |
| 48 | + 792.39764, |
| 49 | + ] |
| 50 | + ] |
| 51 | + with warnings.catch_warnings(): |
| 52 | + warnings.filterwarnings("ignore") |
| 53 | + self.env = grid2op.make("l2rpn_case14_sandbox", test=True, _add_to_name=type(self).__name__) |
| 54 | + self.runner = Runner(**self.env.get_params_for_runner()) |
| 55 | + |
| 56 | + def test_one_episode(self): |
| 57 | + with warnings.catch_warnings(): |
| 58 | + warnings.filterwarnings("ignore") |
| 59 | + _, cum_reward, timestep, max_ts = self.runner.run_one_episode( |
| 60 | + max_iter=self.max_iter |
| 61 | + ) |
| 62 | + assert int(timestep) == self.max_iter |
| 63 | + assert np.abs(cum_reward - self.real_reward) <= self.tol_one, f"{cum_reward} != {self.real_reward}" |
| 64 | + |
| 65 | + def test_one_episode_detailed(self): |
| 66 | + with warnings.catch_warnings(): |
| 67 | + warnings.filterwarnings("ignore") |
| 68 | + _, cum_reward, timestep, max_ts, episode_data = self.runner.run_one_episode( |
| 69 | + max_iter=self.max_iter, detailed_output=True |
| 70 | + ) |
| 71 | + assert int(timestep) == self.max_iter |
| 72 | + assert np.abs(cum_reward - self.real_reward) <= self.tol_one |
| 73 | + for j in range(len(self.all_real_rewards)): |
| 74 | + assert ( |
| 75 | + np.abs(episode_data.rewards[j] - self.all_real_rewards[j]) |
| 76 | + <= self.tol_one |
| 77 | + ), f"{episode_data.rewards[j]} != {self.all_real_rewards[j]}" |
| 78 | + |
| 79 | + def test_2episode(self): |
| 80 | + with warnings.catch_warnings(): |
| 81 | + warnings.filterwarnings("ignore") |
| 82 | + res = self.runner._run_sequential(nb_episode=2, max_iter=self.max_iter) |
| 83 | + assert len(res) == 2 |
| 84 | + for i, (stuff, _, cum_reward, timestep, total_ts) in enumerate(res): |
| 85 | + assert int(timestep) == self.max_iter |
| 86 | + assert np.abs(cum_reward - self.real_reward_li[i]) <= self.tol_one, f"for iter {i}: {cum_reward} != {self.real_reward_li[i]}" |
| 87 | + |
| 88 | + def test_init_from_env(self): |
| 89 | + with warnings.catch_warnings(): |
| 90 | + warnings.filterwarnings("ignore") |
| 91 | + with grid2op.make("rte_case14_test", test=True, _add_to_name=type(self).__name__) as env: |
| 92 | + runner = Runner(**env.get_params_for_runner()) |
| 93 | + res = runner.run(nb_episode=1, max_iter=self.max_iter) |
| 94 | + for i, _, cum_reward, timestep, total_ts in res: |
| 95 | + assert int(timestep) == self.max_iter, f"{timestep} != {self.max_iter}" |
| 96 | + |
| 97 | + def test_seed_seq(self): |
| 98 | + with warnings.catch_warnings(): |
| 99 | + warnings.filterwarnings("ignore") |
| 100 | + with grid2op.make("rte_case14_test", test=True, _add_to_name=type(self).__name__) as env: |
| 101 | + runner = Runner(**env.get_params_for_runner()) |
| 102 | + res = runner.run( |
| 103 | + nb_episode=1, max_iter=self.max_iter, env_seeds=[1], agent_seeds=[2] |
| 104 | + ) |
| 105 | + for i, _, cum_reward, timestep, total_ts in res: |
| 106 | + assert int(timestep) == self.max_iter, f"{timestep} != {self.max_iter}" |
| 107 | + |
| 108 | + def test_seed_par(self): |
| 109 | + with warnings.catch_warnings(): |
| 110 | + warnings.filterwarnings("ignore") |
| 111 | + with grid2op.make("rte_case14_test", test=True, _add_to_name=type(self).__name__) as env: |
| 112 | + runner = Runner(**env.get_params_for_runner()) |
| 113 | + res = runner.run( |
| 114 | + nb_episode=2, |
| 115 | + nb_process=2, |
| 116 | + max_iter=self.max_iter, |
| 117 | + env_seeds=[1, 2], |
| 118 | + agent_seeds=[3, 4], |
| 119 | + ) |
| 120 | + for i, _, cum_reward, timestep, total_ts in res: |
| 121 | + assert int(timestep) == self.max_iter |
| 122 | + |
| 123 | + |
| 124 | +if __name__ == "__main__": |
| 125 | + unittest.main() |
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