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weixin_46229132 2025-03-29 16:53:03 +08:00
parent f347ca8276
commit 3e6887c655
3 changed files with 70 additions and 63 deletions
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1
GA/ga.py
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@ -3,6 +3,7 @@ import random
# import matplotlib.pyplot as plt
import numpy as np
# np.random.seed(42)
class GA(object):

130
env_partion.py
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@ -108,82 +108,88 @@ class PartitionEnv(gym.Env):
# 出现无效调整，直接结束
if not valid_adjust:
return state, reward, True, False, {}
# 调整合理，计算当前时间
else:
if self.partition_step < self.CUT_NUM:
return state, 0.0, False, False, {}
rectangles = self.if_valid_partition()
if not rectangles:
reward = -10
return state, reward, True, False, {}
else:
# 完成 4 步后，判断分区是否合理，并计算各个分区的任务卸载率ρ
valid_partition = True
for i in range(len(self.ori_row_cuts) - 1):
for j in range(len(self.ori_col_cuts) - 1):
d = (self.ori_col_cuts[j+1] - self.ori_col_cuts[j]) * self.W * \
(self.ori_row_cuts[i+1] -
self.ori_row_cuts[i]) * self.H
rho_time_limit = (self.flight_time_factor - self.trans_time_factor) / \
(self.comp_time_factor - self.trans_time_factor)
rho_energy_limit = (self.battery_energy_capacity - self.flight_energy_factor * d - self.trans_energy_factor * d) / \
(self.comp_energy_factor * d -
self.trans_energy_factor * d)
if rho_energy_limit < 0:
valid_partition = False
break
rho = min(rho_time_limit, rho_energy_limit)
# 继续进行路径规划
# 使用遗传算法解多旅行商
best_time, best_path = self.ga_solver(rectangles)
# print(best_time)
# print(best_path)
flight_time = self.flight_time_factor * d
bs_time = self.bs_time_factor * (1 - rho) * d
self.rectangles.append({
'center': ((self.ori_row_cuts[i] + self.ori_row_cuts[i+1]) * self.H / 2, (self.ori_col_cuts[j+1] + self.ori_col_cuts[j]) * self.W / 2),
'flight_time': flight_time,
'bs_time': bs_time,
})
if not valid_partition:
break
if not valid_partition:
reward = -10
return state, reward, True, False, {}
reward = self.BASE_LINE / best_time
if self.partition_step < self.CUT_NUM:
done = False
else:
# 继续进行路径规划
# 使用q_learning解多旅行商
# cities: [[x1, x2, x3...], [y1, y2, y3...]] 城市坐标
# rec_center_lt = [rec_info['center']
# for rec_info in self.rectangles]
# cities = np.column_stack(rec_center_lt)
# cities = np.column_stack((self.center, cities))
done = True
reward = reward * 3
# center_idx = []
# for i in range(self.num_cars - 1):
# cities = np.column_stack((cities, self.center))
# center_idx.append(cities.shape[1] - 1)
return state, reward, done, False, best_path
# tsp = mTSP(params=self.params, num_cities=cities.shape[1], cities=cities, num_cars=self.num_cars,
# center_idx=center_idx, rectangles=self.rectangles)
def if_valid_partition(self):
rectangles = []
for i in range(len(self.ori_row_cuts) - 1):
for j in range(len(self.ori_col_cuts) - 1):
d = (self.ori_col_cuts[j+1] - self.ori_col_cuts[j]) * self.W * \
(self.ori_row_cuts[i+1] -
self.ori_row_cuts[i]) * self.H
rho_time_limit = (self.flight_time_factor - self.trans_time_factor) / \
(self.comp_time_factor - self.trans_time_factor)
rho_energy_limit = (self.battery_energy_capacity - self.flight_energy_factor * d - self.trans_energy_factor * d) / \
(self.comp_energy_factor * d -
self.trans_energy_factor * d)
if rho_energy_limit < 0:
return []
rho = min(rho_time_limit, rho_energy_limit)
# best_time, best_path = tsp.train(self.mTSP_STEPS)
flight_time = self.flight_time_factor * d
bs_time = self.bs_time_factor * (1 - rho) * d
# 使用遗传算法解多旅行商
cities = [self.center]
for rec in self.rectangles:
cities.append(rec['center'])
cities = np.array(cities)
rectangles.append({
'center': ((self.ori_row_cuts[i] + self.ori_row_cuts[i+1]) * self.H / 2, (self.ori_col_cuts[j+1] + self.ori_col_cuts[j]) * self.W / 2),
'flight_time': flight_time,
'bs_time': bs_time,
})
return rectangles
# def q_learning_solver(self):
# 使用q_learning解多旅行商
# cities: [[x1, x2, x3...], [y1, y2, y3...]] 城市坐标
# rec_center_lt = [rec_info['center']
# for rec_info in rectangles]
# cities = np.column_stack(rec_center_lt)
# cities = np.column_stack((self.center, cities))
center_idx = [0]
for i in range(self.num_cars - 1):
cities = np.row_stack((cities, self.center))
center_idx.append(cities.shape[0] - 1)
# center_idx = []
# for i in range(self.num_cars - 1):
# cities = np.column_stack((cities, self.center))
# center_idx.append(cities.shape[1] - 1)
ga = GA(num_drones=self.num_cars, num_city=cities.shape[0], num_total=20,
data=cities, to_process_idx=center_idx, rectangles=self.rectangles)
# tsp = mTSP(params=self.params, num_cities=cities.shape[1], cities=cities, num_cars=self.num_cars,
# center_idx=center_idx, rectangles=rectangles)
best_path, best_time = ga.run()
# best_time, best_path = tsp.train(self.mTSP_STEPS)
# print(best_time)
# print(best_path)
def ga_solver(self, rectangles):
cities = [self.center]
for rec in rectangles:
cities.append(rec['center'])
cities = np.array(cities)
reward = self.BASE_LINE / best_time
center_idx = [0]
for i in range(self.num_cars - 1):
cities = np.row_stack((cities, self.center))
center_idx.append(cities.shape[0] - 1)
return state, reward, True, False, best_path
ga = GA(num_drones=self.num_cars, num_city=cities.shape[0], num_total=20,
data=cities, to_process_idx=center_idx, rectangles=rectangles)
best_path, best_time = ga.run()
return best_time, best_path
def render(self):
if self.phase == 1:

2
human_action.py
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@ -10,7 +10,7 @@ print('state:', state)
# action_series = [[0.67], [0], [0], [0], [0.7]]
# action_series = [0, 0, 3, 0, 10]
action_series = [[0.2], [0.4], [0.7], [0.5]]
# action_series = [[0.2], [0.4], [0.7], [0.5]]
action_series = [[-0.1], [0], [0], [0]]
for i in range(100):