def test_experiences_to_batches(target_computer_class_mock):
compute = target_computer_class_mock.return_value.compute
compute.return_value = np.array([42, 43])
state1 = np.arange(16).reshape((4, 4)) + 1
state2 = np.arange(16).reshape((4, 4)) + 2
state3 = np.arange(16).reshape((4, 4)) + 3
experiences = [Experience(state1, 1, 2, state2, False, False, [3]),
Experience(state2, 3, 4, state3, True, False, [])]
run_inference = Mock(side_effect=[np.array([[0, 0, 0, -0.5],
[0, 0, 0, 0]])])
batcher = ExperienceBatcher(None, run_inference, None, 1.0 / 15.0)
state_batch, targets, actions = batcher.experiences_to_batches(experiences)
reward_batch = np.array([2, 4])
bad_action_batch = np.array([False, True])
next_state_batch = np.array([state2.flatten(), state3.flatten()]) / 15.0
available_actions_batch = np.array([[False, False, False, True],
[False, False, False, False]])
assert (compute.call_args_list[0][0][0] == reward_batch).all()
assert (compute.call_args_list[0][0][1] == bad_action_batch).all()
assert (compute.call_args_list[0][0][2] == next_state_batch).all()
assert (compute.call_args_list[0][0][3] == available_actions_batch).all()
expected_state_batch = np.array([state1.flatten(), state2.flatten()]) / 15.0
assert (state_batch == expected_state_batch).all()
assert (targets == np.array([42, 43])).all()
assert (actions == np.array([1, 3])).all()
def test_experiences_to_batches(target_computer_class_mock):
compute = target_computer_class_mock.return_value.compute
compute.return_value = np.array([42, 43])
state1 = np.arange(16).reshape((4, 4)) + 1
state2 = np.arange(16).reshape((4, 4)) + 2
state3 = np.arange(16).reshape((4, 4)) + 3
experiences = [
Experience(state1, 1, 2, state2, False, False, [3]),
Experience(state2, 3, 4, state3, True, False, [])
]
run_inference = Mock(
side_effect=[np.array([[0, 0, 0, -0.5], [0, 0, 0, 0]])])
batcher = ExperienceBatcher(None, run_inference, None, 1.0 / 15.0)
state_batch, targets, actions = batcher.experiences_to_batches(experiences)
reward_batch = np.array([2, 4])
bad_action_batch = np.array([False, True])
next_state_batch = np.array([state2.flatten(), state3.flatten()]) / 15.0
available_actions_batch = np.array([[False, False, False, True],
[False, False, False, False]])
assert (compute.call_args_list[0][0][0] == reward_batch).all()
assert (compute.call_args_list[0][0][1] == bad_action_batch).all()
assert (compute.call_args_list[0][0][2] == next_state_batch).all()
assert (compute.call_args_list[0][0][3] == available_actions_batch).all()
expected_state_batch = np.array([state1.flatten(),
state2.flatten()]) / 15.0
assert (state_batch == expected_state_batch).all()
assert (targets == np.array([42, 43])).all()
assert (actions == np.array([1, 3])).all()
