code: # Copyright 2019 DeepMind Technologies Limited
  #
  # Licensed under the Apache License, Version 2.0 (the "License");
  # you may not use this file except in compliance with the License.
  # You may obtain a copy of the License at
  #
  #      http://www.apache.org/licenses/LICENSE-2.0
  #
  # Unless required by applicable law or agreed to in writing, software
  # distributed under the License is distributed on an "AS IS" BASIS,
  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
  # See the License for the specific language governing permissions and
  # limitations under the License.
  
  """Tests for open_spiel.python.algorithms.double_oracle."""
  
  from absl.testing import absltest
  import numpy as np
  
  from open_spiel.python.algorithms import double_oracle
  import pyspiel
  
  
  class DoubleOracleTest(absltest.TestCase):
  
    def test_rock_paper_scissors(self):
      game = pyspiel.load_matrix_game("matrix_rps")
      solver = double_oracle.DoubleOracleSolver(game)
      solution, iteration, value = solver.solve(initial_strategies=[[0], [0]])
      np.testing.assert_allclose(solution[0], np.ones(3)/3.)
      np.testing.assert_allclose(solution[1], np.ones(3)/3.)
      self.assertEqual(iteration, 3)
      self.assertAlmostEqual(value, 0.0)
  
    def test_single_step(self):
      game = pyspiel.load_matrix_game("matrix_rps")
      solver = double_oracle.DoubleOracleSolver(game)
      solver.subgame_strategies = [[0], [0]]
      best_response, best_response_utility = solver.step()
      self.assertListEqual(best_response, [1, 1])
      self.assertListEqual(best_response_utility, [1.0, 1.0])
  
    def test_kuhn_poker(self):
      game = pyspiel.extensive_to_matrix_game(pyspiel.load_game("kuhn_poker"))
      solver = double_oracle.DoubleOracleSolver(game)
      solution, iteration, value = solver.solve(initial_strategies=[[0], [0]])
  
      # check if solution is Nash
      exp_utilty = solution[0] @ solver.payoffs @ solution[1]
      self.assertAlmostEqual(max(solver.payoffs[0] @ solution[1]), exp_utilty[0])
      self.assertAlmostEqual(max(solution[0] @ solver.payoffs[1]), exp_utilty[1])
  
      self.assertEqual(iteration, 8)
      self.assertAlmostEqual(value, 0.0)
  
  
  if __name__ == "__main__":
    absltest.main()
  
apis: numpy.ones