Added initial test

open_spiel/python/algorithms/efr_test.py

@@ -0,0 +1,112 @@
+# Copyright 2023 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.efr."""
+
+import itertools
+
+from absl.testing import absltest
+from absl.testing import parameterized
+import numpy as np
+
+from open_spiel.python import policy
+from open_spiel.python.algorithms import efr
+from open_spiel.python.algorithms import expected_game_score
+from open_spiel.python.algorithms import exploitability
+import pyspiel
+
+_KUHN_GAME = pyspiel.load_game("kuhn_poker")
+_LEDUC_GAME = pyspiel.load_game("leduc_poker")
+
+_KUHN_UNIFORM_POLICY = policy.TabularPolicy(_KUHN_GAME)
+_LEDUC_UNIFORM_POLICY = policy.TabularPolicy(_LEDUC_GAME)
+
+
+class ModuleLevelFunctionTest(absltest.TestCase):
+
+  def test__update_current_policy(self):
+    game = pyspiel.load_game("kuhn_poker")
+    tabular_policy = policy.TabularPolicy(game)
+
+    cumulative_regrets = np.arange(0, 12 * 2).reshape((12, 2))
+    expected_policy = cumulative_regrets / np.sum(
+        cumulative_regrets, axis=-1, keepdims=True)
+    nodes_indices = {
+        u"0": 0,
+        u"0pb": 1,
+        u"1": 2,
+        u"1pb": 3,
+        u"2": 4,
+        u"2pb": 5,
+        u"1p": 6,
+        u"1b": 7,
+        u"2p": 8,
+        u"2b": 9,
+        u"0p": 10,
+        u"0b": 11,
+    }
+    # pylint: disable=g-complex-comprehension
+    info_state_nodes = {
+        key: efr._InfoStateNode(
+            legal_actions=[0, 1],
+            index_in_tabular_policy=None,
+            cumulative_regret=dict(enumerate(cumulative_regrets[index])),
+            cumulative_policy=None) for key, index in nodes_indices.items()
+    }
+    available_deviations = ["blind action", "informed action", "blind cf", "informed cf", "bps", "cfps", "csps", "tips", "bhv"]
+
+    # pylint: enable=g-complex-comprehension
+
+    efr._update_current_policy(tabular_policy, info_state_nodes)
+
+    np.testing.assert_array_equal(expected_policy,
+                                  tabular_policy.action_probability_array)
+
+
+class EFRTest(parameterized.TestCase, absltest.TestCase):
+
+  @parameterized.parameters(
+      ["blind action", "informed action", "blind cf", "informed cf", "bps", "cfps", "csps", "tips", "bhv"])
+  def test_policy_zero_is_uniform(self):
+    # We use Leduc and not Kuhn, because Leduc has illegal actions and Kuhn does
+    # not.
+    game = pyspiel.load_game("leduc_poker")
+    cfr_solver = efr._EFRSolver(
+        game,
+        deviations_name=deviations_name
+        )
+
+    np.testing.assert_array_equal(
+        _LEDUC_UNIFORM_POLICY.action_probability_array,
+        cfr_solver.current_policy().action_probability_array)
+    np.testing.assert_array_equal(
+        _LEDUC_UNIFORM_POLICY.action_probability_array,
+        cfr_solver.average_policy().action_probability_array)
+
+  @parameterized.parameters(
+      ["blind cf", "informed cf", "bps", "cfps", "csps", "tips", "bhv"])
+  def test_cfr_kuhn_poker(self):
+    game = pyspiel.load_game("kuhn_poker")
+    efr_solver = efr.EFRSolver(game)
+    for _ in range(300):
+      efr_solver.evaluate_and_update_policy()
+    average_policy = efr_solver.average_policy()
+    average_policy_values = expected_game_score.policy_value(
+        game.new_initial_state(), [average_policy] * 2)
+    # 1/18 is the Nash value. See https://en.wikipedia.org/wiki/Kuhn_poker
+    np.testing.assert_allclose(
+        average_policy_values, [-1 / 18, 1 / 18], atol=1e-3)
+
+if __name__ == "__main__":
+  absltest.main()