def _add_automated_vehicles(self):
"""Replace a portion of vehicles with automated vehicles."""
penetration = self.env_params.additional_params["rl_penetration"]
# Sort the initial vehicles by their positions.
sorted_vehicles = sorted(self.k.vehicle.get_ids(),
key=lambda x: self.k.vehicle.get_x_by_id(x))
# Replace every nth vehicle with an RL vehicle.
for lane in range(self._num_lanes):
sorted_vehicles_lane = [
veh for veh in sorted_vehicles if get_lane(self, veh) == lane
]
if isinstance(self.k.network.network, I210SubNetwork):
# Choose a random starting position to allow for stochasticity.
i = random.randint(0, int(1 / penetration) - 1)
else:
i = 0
for veh_id in sorted_vehicles_lane:
self.k.vehicle.set_vehicle_type(veh_id, "human")
i += 1
if i % int(1 / penetration) == 0:
# Don't add vehicles past the control range.
pos = self.k.vehicle.get_x_by_id(veh_id)
if pos < self._control_range[1]:
self.k.vehicle.set_vehicle_type(veh_id, "rl")
def _add_automated_vehicles(self):
"""Replace a portion of vehicles with automated vehicles."""
penetration = self.env_params.additional_params["rl_penetration"]
# Sort the initial vehicles by their positions.
sorted_vehicles = sorted(self.k.vehicle.get_ids(),
key=lambda x: self.k.vehicle.get_x_by_id(x))
# Replace every nth vehicle with an RL vehicle.
for lane in range(self._num_lanes):
sorted_vehicles_lane = [
veh for veh in sorted_vehicles if get_lane(self, veh) == lane
]
for i, veh_id in enumerate(sorted_vehicles_lane):
if (i + 1) % int(1 / penetration) == 0:
# Don't add vehicles past the control range.
pos = self.k.vehicle.get_x_by_id(veh_id)
if pos < self._control_range[1]:
self.k.vehicle.set_vehicle_type(veh_id, "rl")
def compute_reward(self, rl_actions, **kwargs):
"""See class definition."""
# In case no vehicles were available in the current step, pass an empty
# reward dict.
if rl_actions is None:
return {}
reward = {}
# Collect the names of the vehicles within the control range.
control_min = self._control_range[0]
control_max = self._control_range[1]
veh_ids = [
veh_id for veh_id in self.k.vehicle.get_ids()
if control_min <= self.k.vehicle.get_x_by_id(veh_id) <= control_max
]
for lane in range(self._num_lanes):
# Collect the names of all vehicles on the given lane, while
# taking into account edges with an extra lane.
veh_ids_lane = [v for v in veh_ids if get_lane(self, v) == lane]
# Collect the names of the RL vehicles on the lane.
rl_ids = [veh for veh in self.rl_ids() if veh in veh_ids_lane]
# Collect the actions that just correspond to this lane.
rl_actions_lane = {
key: rl_actions[key]
for key in rl_actions.keys() if key in rl_ids
}
# Compute the reward for a given lane.
reward["lane_{}".format(lane)] = self._compute_reward_util(
rl_actions=rl_actions_lane,
veh_ids=veh_ids_lane,
rl_ids=rl_ids,
**kwargs)
return reward
def additional_command(self):
"""See parent class.
Here, the operations are done at a per-lane level.
"""
for lane in range(self._num_lanes):
# Collect the names of the RL vehicles on the given lane, while
# tacking into account edges with an extra lane.
rl_ids = [
veh for veh in self.k.vehicle.get_rl_ids()
if get_lane(self, veh) == lane
]
# Update the RL lists.
self.rl_queue[lane], self.rl_veh[lane], self.removed_veh = \
update_rl_veh(
self,
rl_queue=self.rl_queue[lane],
rl_veh=self.rl_veh[lane],
removed_veh=self.removed_veh,
control_range=self._control_range,
num_rl=self.num_rl,
rl_ids=reversed(sorted(
rl_ids, key=self.k.vehicle.get_x_by_id)),
)
# Specify actions for the uncontrolled RL vehicles based on human-
# driven dynamics.
for veh_id in list(set(rl_ids) - set(self.rl_veh[lane])):
self._rl_controller.veh_id = veh_id
acceleration = self._rl_controller.get_action(self)
self.k.vehicle.apply_acceleration(veh_id, acceleration)
# Specify observed vehicles.
for veh_id in self.leader + self.follower:
self.k.vehicle.set_observed(veh_id)