Observers

BARK-ML observers convert the BARK world into a suitable input representation for machine learning, such as a vectorial representation that can be used by deep neural networks. There are several observers available ranging from concatenated state vector to graph representations.

The observer base class (BaseObserver) looks as follows:

class BaseObserver(ABC):
  def __init__(self,
               params):
    self._params = params

  @abstractmethod
  def Observe(self, observed_world):
    """Generates an observation for the BARK world

    Arguments:
        world {bark.ObservedWorld} -- observed BARK world

    Returns:
        e.g., np.array, graph, ...
    """
    pass

  def Reset(self, world):
    pass

  @property
  def observation_space(self):
    pass

Each Observer in BARK has to implement two functions (Observe and Reset) and the property observation_space. The Observe function returns a machine learning friendly input representation, such as a numpy array or graph. Observers that trace vehicles over the course of an episode might require the Reset function to be overloaded to, e.g., reset member variables. And, finally, the property observation_space needs to be defined that tells the learning agent the dimension of the input space.

BARK-ML implements currently these observers:

• NearestAgentsObserver: Concatenates the states of the n-nearest agents that are within a defined threshold radius.
• NearestObserver: C++ observer that concatenates the states of the n-nearest agents that are within a defined threshold radius.
• SimpleObserver: Concatenates the states of agents in the environment and returns a row vector.
• GraphObserver: Returns a directed graph of N vehicles.