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city.py
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city.py
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import dgl
import torch
from miscellaneous import *
import random
import networkx as nx
from math_utils import euclidean_distance, cartesian
from scipy import spatial
class City:
def __init__(self,
G: dgl.DGLGraph,
speed_info=None,
name='simple_city',
consider_speed=False,
verbose=False,
after_action_random=True,
**kwargs
):
'''
RL environment for road network.
:param G: line graph of road graph.
:param speed_info: SpeedInfo object
:param name: name for city
:param verbose: print debugging message
:param after_action_random: after action, put driver on random position or not.
'''
self.name = name
self.roads: list[Road] = []
self.drivers: list[Person] = []
self.city_time = 0
self.city_time_unit_in_minute = 1
self.updated_plan_interval = 15
self.G = G
places = []
self.idx_ids = []
for node, data in G.nodes(data=True):
idx_ids.append(node)
coordinates = [data["x"], data["y"]]
cartesian_coord = cartesian(*coordinates)
places.append(cartesian_coord)
self.tree = spatial.KDTree(places)
self.N = G.number_of_nodes()
self.total_agent = len(self.drivers)
pops = []
self.road_key_dict = {}
for i in range(self.N):
road = Road(i, **self.G.nodes[i].data)
self.roads.append(road)
self.road_key_dict[(road.u, road.v)] = road.uuid
self.actionable_drivers: list[Person] = []
self.non_actionable_drivers: list[Person] = []
self.epsilon = 0
self.seed = 0
self.random_seed = True
self.driver_initializer = driver_initializer
self.speed_info = speed_info
self.verbose = verbose
self.after_action_random = after_action_random
def get_observation(self):
#depends on road
# obs = torch.zeros((self.N, 3),device=torch.device('cuda'))
# for i in range(self.N):
# obs[i][0] = len(self.roads[i].demands)
# obs[i][1] = len(self.roads[i].drivers)
# obs[i][2] = self.roads[i].speed / 24
# return obs
obs = torch.zeros((self.total_agent, 2),device=torch.device('cuda'))
for driver in self.drivers:
obs[i][0] = int(driver.is_online())
obs[i][1] = driver.plan.activities[driver.current_activity].legs[0].score if driver.is_online() else 100
return obs
def set_speed(self):
if self.speed_info is not None:
self.speed_info.set_speed(self)
def get_road(self, u, v):
road_id = self.road_key_dict[(u, v)]
return road_id
def update_drivers_status(self):
'''
Check whether driver has arrived
:return:
'''
for driver in self.drivers:
if driver.is_online() and driver.on_road:
act=driver.plan.activities[driver.current_activity]
if(driver.road_index==act.legs[act.selected_leg].end_link and driver.road_position>=act.legs[act.selected_leg].end_position):
driver.on_road=False #arrived
driver.current_activity+=1 #do another activity
def get_actionable_drivers(self):
'''
get actionable / non actionable drivers
:return: list of actionable / non actionable drivers
'''
actionable_drivers = []
actionable_drivers_count = 0
non_actionable_drivers = []
non_actionable_drivers_count = 0
for driver in self.drivers:
if driver.on_road:
if(driver.movable_time > 0):
road = self.roads[driver.road_index]
left_distance = road.length - driver.road_position
road_speed_in_meter_per_min = road.speed * 1000 / 60
time_to_finish = left_distance / road_speed_in_meter_per_min
if time_to_finish > driver.movable_time:
driver.road_position += driver.movable_time * road_speed_in_meter_per_min
driver.movable_time = 0
else:
driver.movable_time -= time_to_finish
if driver.road_index+1 < len(driver.route):
next_road_index = self.road_key_dict[driver.route[driver.road_index+1]]
else:
next_road_index = None
if(next_road_index!=None):
self.roads[self.road_key_dict[driver.route[driver.road_index]]].drivers.remove(driver)
self.roads[next_road_index].drivers.append(driver)
driver.road_index+=1
if self.after_action_random:
driver.road_position = np.random.random() * self.roads[next_road_index].length
else:
road_speed_in_meter_per_min = self.roads[next_road_index].speed * 1000.0 / 60.0
x = max(0.0, driver.movable_time - 0.3) * road_speed_in_meter_per_min
max_x = 0.9 * self.roads[next_road_index].length
min_x = 0.1 * self.roads[next_road_index].length
x = max(min(x, max_x), min_x)
next_position_ratio += (x / (self.roads[next_road_index].length + 0.01))
total_counts += 1
driver.road_position = x
driver.movable_time = 0
non_actionable_drivers.append(driver)
non_actionable_drivers_count += 1
else:
actionable_drivers.append(driver)
actionable_drivers_count += 1
if self.verbose:
print("Actionable driver number :", sum(actionable_drivers_count))
print("Non-Actionable driver number :", sum(non_actionable_drivers_count))
return actionable_drivers, non_actionable_drivers
def routing(self,start_point,end_point, k_num = 3):
cartesian_coord_a = cartesian(start_point[0], start_point[1])
cartesian_coord_b = cartesian(end_point[0], end_point[1])
distance = euclidean_distance(cartesian_coord_a, cartesian_coord_b)
closest_a = self.tree.query([cartesian_coord_b], p=2)
closest_b = self.tree.query([cartesian_coord_a], p=2)
def dist(a, b):
c_a = cartesian(self.G.nodes[a]["x"], self.G.nodes[a]["y"])
c_b = cartesian(self.G.nodes[b]["x"], self.G.nodes[b]["y"])
return euclidean_distance(c_a, c_b)
# sp = nx.astar_path(G, idx_ids[closest_a[1][0]], idx_ids[closest_b[1][0]], heuristic=dist, weight="time")
temps = []
found_paths = []
for i in range(k_num):
if i == 0:
sp = nx.astar_path(self.G, self.idx_ids[closest_a[1][0]], self.idx_ids[closest_b[1][0]], heuristic=dist, weight="length")
else:
sp = nx.astar_path(self.G, self.idx_ids[closest_a[1][0]], self.idx_ids[closest_b[1][0]], heuristic=dist, weight="time")
pathGraph = nx.path_graph(sp)
paths = pathGraph.edges()
found_paths.append(paths)
if i > 0:
temps.append(list(paths))
# mutate time
for path in paths:
u, v = path
self.G[u][v]["time"] = 99
# restore time
for p in list(set(sum(temps, []))):
u, v = p
self.G[u][v]["time"] = (G[u][v]["length"] / G[u][v]["avg_speed"]) * 60
return found_paths
def apply_policy(self, policy):
'''
Apply policy to controllable agents
:param policy: list of policy for all agents.
:return:
'''
#apply action for actionable drivers
next_position_ratio = 0
total_counts = 0
action_threshold=0.5
for index,driver in enumerate(self.city.drivers):
if driver.is_online():
# uniformly random (probability of epsilon)
if policy is None or (self.epsilon > 0 and np.random.binomial(1, self.epsilon) == 1):
action = np.random.choice([0,1])
# random from stochastic policy (probability of 1 - epsilon)
else:
#greedy
action = int(np.max(policy[index])>action_threshold)
if action:
if(driver.current_activity<len(driver.plan.activities)):
#let's fucking go!
driving_legs = driver.plan.activities[driver.current_activity+1].legs
# randomly selecting a leg
# TODO selecting a leg with no side effect to the network
selected_leg=np.random.choice(len(driving_legs))
driver.plan.activities[driver.current_activity+1].selected_leg=selected_leg
driver.route = driving_legs[selected_leg]
driver.on_road=True
#teleported to the first road on random location
driver.road_index = 0
driver.road_position = np.random.random() * self.roads[self.road_key_dict[driver.route[driver.road_index]]].length
if self.verbose and not self.after_action_random:
print("After movement position ratio average:", next_position_ratio / total_counts)
def current_total_call_number(self):
n = 0
for road in self.roads:
n += len(road.calls)
return n
def current_total_driver_number(self):
online = 0
available = 0
offline = 0
for driver in self.drivers:
if driver.on_road:
online += 1
elif driver.is_online(self.city_time):
offline +=1
else:
available += 1
return online + available + offline, online, available, offline
def reset(self):
'''
Clear all drivers, calls
:return:
'''
self.city_time = 0
for road_index in range(self.N):
road = self.roads[road_index]
road.drivers.clear()
road.demands.clear()
self.drivers.clear()
# def get_next_driver_id(self):
# self.driver_uuid += 1
# return self.driver_uuid - 1
def initialize(self):
'''
Generate agents
:return: initial state
'''
#TODO
# generate idle drivers
# for road_index in range(self.N):
# number_of_drivers = int(driver_distribution[road_index]) #np.random.choice([0,1,2,3],p=[0.5,0.2,0.2,0.1])
# road = self.roads[road_index]
# for _ in range(number_of_drivers):
# driver = Driver(self.get_next_driver_id(), road_index, np.random.random() * road.length)
# road.drivers.append(driver)
# self.drivers.append(driver)
# print("City initialized with total %d drivers" % len(self.drivers))
self.set_speed()
return self.get_observation()
def charge_drive_time(self):
for driver in self.drivers:
if not driver.is_online() and driver.on_road:
driver.movable_time = self.city_time_unit_in_minute
def generate_plan(self):
for driver in self.drivers:
if driver.is_online():
current_act=driver.driver.plan.activities[driver.current_activity]
source=current_act.location
destination=driver.plan.activities[driver.current_activity+1].location
paths=self.routing(source, destination)
current_act.legs=paths
def step(self, policy):
'''
Single update cycle.
:param policy: list of policy for all roads.
:return: next state, assigned call number, missed call number
'''
self.charge_drive_time() #riders move
self.apply_policy(policy)
self.actionable_drivers, self.non_actionable_drivers = self.get_actionable_drivers()
self.city_time += 1
t, a, b, c = self.current_total_driver_number()
if self.verbose:
print(self.city_time)
print("Total driver %d, on_road %d, available %d , do_act %d" % (t, a, b, c))
# before = self.current_total_call_number()
# self.update_old_calls()
# after = self.current_total_call_number()
# missed_call_number = before - after
if self.city_time%self.updated_plan_interval==0:
#update plan for online drivers
self.generate_plan()
self.update_drivers_status()
self.set_speed()
next_state = self.get_observation()
# return next_state, assigned_call_number, missed_call_number
return next_state