def METHOD_NAME(test_vector, mocker, _seed):
    batch_size, discount, is_movingavg, ref_policy_loss, ref_value_loss = test_vector.values()
    mocked_trace = mocker.patch("nncf.torch.automl.agent.ddpg.memory.SequentialMemory.sample_and_split")
    # state_batch, action_batch, reward_batch, next_state_batch, terminal_batch
    mocked_trace.return_value = (
        np.ones((batch_size, N_STATE)),
        np.ones((batch_size, N_ACTION)),
        np.ones((batch_size, SCALAR_ONE)),
        np.ones((batch_size, N_STATE)),
        np.ones((batch_size, SCALAR_ONE)),
    )
    hparams = {
        "hidden1": N_HIDDEN_NODE,
        "hidden2": N_HIDDEN_NODE,
        "bsize": batch_size,
        "discount": discount,
        "window_length": SCALAR_ONE,
    }
    ddpg = DDPG(N_STATE, N_ACTION, hparam_override=hparams)
    ddpg.actor.load_state_dict({state: torch.ones_like(param) / 2 for state, param in ddpg.actor.state_dict().items()})
    ddpg.actor_target.load_state_dict(
        {state: torch.ones_like(param) for state, param in ddpg.actor_target.state_dict().items()}
    )
    ddpg.moving_average = is_movingavg
    ddpg.update_policy()
    np.testing.assert_almost_equal(ddpg.policy_loss.item(), ref_policy_loss)
    np.testing.assert_almost_equal(ddpg.value_loss.item(), ref_value_loss)