def __init__(self,
master_kernel,
sim_params):
"""See parent class."""
KernelVehicle.__init__(self, master_kernel, sim_params)
self.__ids = [] # ids of all vehicles
self.__human_ids = [] # ids of human-driven vehicles
self.__controlled_ids = [] # ids of flow-controlled vehicles
self.__controlled_lc_ids = [] # ids of flow lc-controlled vehicles
self.__rl_ids = [] # ids of rl-controlled vehicles
self.__observed_ids = [] # ids of the observed vehicles
# vehicles: Key = Vehicle ID, Value = Dictionary describing the vehicle
# Ordered dictionary used to keep neural net inputs in order
self.__vehicles = collections.OrderedDict()
# create a sumo_observations variable that will carry all information
# on the state of the vehicles for a given time step
self.__sumo_obs = {}
# total number of vehicles in the network
self.num_vehicles = 0
# number of rl vehicles in the network
self.num_rl_vehicles = 0
# contains the parameters associated with each type of vehicle
self.type_parameters = {}
# contain the minGap attribute of each type of vehicle
self.minGap = {}
# list of vehicle ids located in each edge in the network
self._ids_by_edge = dict()
# list of vehicle ids located in each lane in the network
self._ids_by_lane = dict()
# number of vehicles that entered the network for every time-step
self._num_departed = []
self._departed_ids = []
# number of vehicles to exit the network for every time-step
self._num_arrived = []
self._arrived_ids = []
self._arrived_rl_ids = []
# whether or not to automatically color vehicles
try:
self._color_by_speed = sim_params.color_by_speed
self._force_color_update = sim_params.force_color_update
except AttributeError:
self._force_color_update = False
# old speeds used to compute accelerations
self.previous_speeds = {}
def __init__(self, master_kernel, sim_params):
"""See parent class.
Copied from `flow.core.kernel.vehicle.traci`"""
KernelVehicle.__init__(self, master_kernel, sim_params)
self.__ids = [] # ids of all vehicles
self.__human_ids = [] # ids of human-driven vehicles
self.__controlled_ids = [] # ids of flow-controlled vehicles
self.__controlled_lc_ids = [] # ids of flow lc-controlled vehicles
self.__rl_ids = [] # ids of rl-controlled vehicles
self.__observed_ids = [] # ids of the observed vehicles
# vehicles: Key = Vehicle ID, Value = Dictionary describing the vehicle
# Ordered dictionary used to keep neural net inputs in order
self.__vehicles = collections.OrderedDict()
# create a sumo_observations variable that will carry all information
# on the state of the vehicles for a given time step
self.__sumo_obs = {}
# total number of vehicles in the network
self.num_vehicles = 0
# number of rl vehicles in the network
self.num_rl_vehicles = 0
# contains the parameters associated with each type of vehicle
self.type_parameters = {}
# list of vehicle ids located in each edge in the network
self._ids_by_edge = dict()
# number of vehicles that entered the network for every time-step
self._num_departed = []
self._departed_ids = []
# number of vehicles to exit the network for every time-step
self._num_arrived = []
self._arrived_ids = []