def save_timing_performance(timing, filename):
full_timings = [3] * 8
full_timings[0] = timing[0]
full_timings[4] = timing[1]
traffic_light = PhaseModifier("node1")
controller = StaticTrafficLightController(controller=traffic_light,
sequence=list(range(8)),
timings=full_timings)
sim = Simulator()
sim.add_simulation_component(SimulationOutputParser)
sim.add_tickable(controller)
sim.run(sumocfg1, gui=False)
sim.save_results(filename)
def evaluate_timing(timing):
traffic_light = PhaseModifier("node1")
full_timings = [3] * 8
full_timings[0] = timing[0]
full_timings[4] = timing[1]
controller = StaticTrafficLightController(controller=traffic_light,
sequence=list(range(8)),
timings=full_timings)
sim = Simulator()
sim.add_simulation_component(SimulationOutputParser)
sim.add_tickable(controller)
if not sim.run(sumocfg1, time_steps=2000, gui=False):
return sim.results
return False
from reward import RewardCalculator
from simulation import Simulator
import numpy as np
from stats.output_parser import SimulationOutputParser
from gradient_descent.static_controller import StaticTrafficLightController
from action import PhaseModifier
sim = Simulator()
sim.add_simulation_component(SimulationOutputParser)
t = [3]*8
t[0] = 50
t[4] = 50
print(t)
controller = StaticTrafficLightController(PhaseModifier("node1"),list(range(8)),t)
sim.add_tickable(controller)
sumocfg1 = "..\\test_environments\\single_intersection_random_trips\\newnet.sumocfg"
sumocfg2 = "..\\test_environments\\grid_map\\4by4.sumocfg"
sim.run(sumocfg1, gui=False)
print(sim.results["mean_speed"].var()**.5)
print(sim.results["duration"].var()**.5)
print(timing)
chromosome = Chromosome(timing, fitness=0)
configuration = []
for ii in range(8):
configuration.append(3)
if ii == 0:
configuration[ii] = timing[0]
if ii == 4:
configuration[ii] = timing[1]
chromosome_controller = StaticTrafficLightController(
PhaseModifier("node1"), list(range(0, 8)), configuration)
print(chromosome._phases_steps)
print(configuration)
sim = Simulator()
sim.add_tickable(chromosome_controller)
sim.run(sumocfg1, gui=False)
fitness = XMLDataExtractor(path).get_data()
chromosome.set_fitness(fitness)
population.append(chromosome)
for member in population:
print(member._fitness)
best_initial_solution = min(population, key=lambda x: x._fitness)
configuration = []
for ii in range(8):
configuration.append(3)
if ii == 0:
def __init__(self, reward_calc, state, controller, actions):
super().__init__(reward_calc, state, controller, actions)
import itertools
self._phases = itertools.cycle([0] * 30 + [4] * 30)
def _select_action(self, state):
return next(self._phases)
if __name__ == "__main__":
class X:
def __init__(self, s):
self.x = 0
self.s = s
def tick(self):
x = int(input("Enter a phase :"))
self.s.do_action(x)
sim = Simulator()
rc = RewardCalculator()
sim.add_tickable(rc)
c = Controller(PhaseModifier("node1"))
s = StateGenerator()
agent = SingleIntersectQAgent(rc, s, c, [0, 3, 4])
sim.add_tickable(agent)
sim.add_tickable(X(c))
sumocfg = "..\\..\\test_environments\\single_intersection_map\\newnet.sumocfg"
sim.run(sumocfg, time_steps=1000, gui=True)
