Dqn tutorial with multi dimensional actions

Hi everybody,

at the moment I am learning Reinforcement Learning with tf-agents.

I implemented a custom env with three ations and state which consists of three states:

class CardGameEnv(py_environment.PyEnvironment):

  def __init__(self):
    self._action_spec = array_spec.BoundedArraySpec(
        shape=(3,), dtype=np.int32, minimum=[0,0,0], maximum=[1,1,1], name='action')
    self._observation_spec = array_spec.BoundedArraySpec(
        shape=(1,3), dtype=np.float32, minimum=0, name='observation')
    self._state = [0, 0, 0]
    self._episode_ended = False

  def action_spec(self):
    return self._action_spec

  def observation_spec(self):
    return self._observation_spec

  def _reset(self):
    self._state = [0, 0, 0]
    self._episode_ended = False
    return ts.restart(np.array([self._state], dtype=np.float32))

  def _step(self, action):

    if self._episode_ended:
      # The last action ended the episode. Ignore the current action and start
      # a new episode.
      return self.reset()

    # check action
    print('Action: ', action)
    print('Action data type: ', type(action))

    # calc state
    print('State before: ', self._state)
    self._state[0] = self._state[0] + action[0]  
    self._state[1] = self._state[1] + action[1]
    self._state[2] = self._state[2] + action[2]
    print('State after: ', self._state)    

    if self._state[3] >= 5.0:
      self._episode_ended = True
      reward = self._state[0] - 21
      return ts.termination(np.array([self._state], dtype=np.float32), reward)
      return ts.transition(
          np.array([self._state], dtype=np.float32), reward=0.0, discount=1.0)

Now I want to coulpe the env with a DQN agent.
But all tutorials that I found work with envs with only one action.
For example: Train a Deep Q Network with TF-Agents  |  TensorFlow Agents
Is there a tutorial describing a more advance example?
Thanks in advance!