class_docstr: Unit tests for policy gradient.
class_docstr_tok: ["Unit","tests","for","policy","gradient","."]
code: def test_update_policy_and_value_function(self):
      action_space = spaces.Discrete(2)
      observation_space = spaces.Box(0, 1, (1,))
      sut = ActorCritic("", observation_space, action_space)
  
      # Set up.
      self._setup_trajectory(sut)
      sut._process_accumulated_trajectory(True)
      sut._trainer = MagicMock()
      sut._adjust_learning_rate = MagicMock()
  
      # Call test method.
      sut._update_networks()
  
      # Verify value network behavior.
      self.assertEqual(sut._trainer.train_minibatch.call_count, 1)
      call_args = sut._trainer.train_minibatch.call_args
      np.testing.assert_array_equal(
          call_args[0][0][sut._input_variables],
          [np.array([0.1], np.float32), np.array([0.2], np.float32)],
      )
      np.testing.assert_array_almost_equal(
          call_args[0][0][sut._value_network_output_variables],
          [[2.9975], [3.05]],
      )
      np.testing.assert_array_equal(
          call_args[0][0][sut._policy_network_output_variables],
          [np.array([1, 0], np.float32), np.array([0, 1], np.float32)],
      )
      np.testing.assert_array_almost_equal(
          call_args[0][0][sut._policy_network_weight_variables],
          [[0.9975], [2.05]],
      )
  
      # Verify data buffer size.
      self.assertEqual(len(sut._input_buffer), 0)
  
imports: import unittest
  import cntk.contrib.deeprl.tests.spaces
  import numpy
  from cntk.contrib.deeprl.agent.policy_gradient import ActorCritic
  from cntk.layers import Dense
  from cntk.losses import cross_entropy_with_softmax
  from cntk.ops import input_variable, placeholder
label_desc: Use image node_id 12 for calling the PolicyGradientTest obj's underlying member method code with example usage: obj.test_update_policy_and_value_function() without return types