改成50_50_3场景
This commit is contained in:
weixin_46229132
parent
d64ec83042
commit
6a82010112
@ -10,7 +10,7 @@ import torch
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import sys
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import os
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sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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from env_partion_dist import PartitionEnv
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from env_partion_dist1 import PartitionEnv
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# fmt: on
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'''Hyperparameter Setting'''
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@ -47,7 +47,7 @@ def save_best_solution(info_lt):
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# 读取已有的最优解
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try:
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with open('solutions/dqn_params_100_100_6.json', 'r') as f:
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with open('solutions/dqn_params_50_50_3.json', 'r') as f:
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saved_solution = json.load(f)
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saved_time = saved_solution['best_time']
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except FileNotFoundError:
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4
GA/ga.py
4
GA/ga.py
@ -16,7 +16,7 @@ class GA(object):
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self.location = data
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self.to_process_idx = to_process_idx
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self.rectangles = rectangles
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self.epochs = 1000
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self.epochs = 500
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self.ga_choose_ratio = 0.2
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self.mutate_ratio = 0.05
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# fruits中存每一个个体是下标的list
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@ -314,7 +314,7 @@ class GA(object):
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early_stop_cnt = 0
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else:
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early_stop_cnt += 1
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if early_stop_cnt == 100: # 若连续50次没有性能提升，则早停
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if early_stop_cnt == 150: # 若连续50次没有性能提升，则早停
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break
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self.best_record.append(1.0 / best_score)
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best_length = 1.0 / best_score
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93
GA/use_ga.py
Normal file
93
GA/use_ga.py
Normal file
@ -0,0 +1,93 @@
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import random
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import math
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import yaml
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import numpy as np
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from utils import if_valid_partition, GA_solver
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from itertools import product, combinations
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import json
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from tqdm import tqdm
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np.random.seed(42)
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random.seed(42)
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best_T = float('inf')
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best_solution = None
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best_row_boundaries = None
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best_col_boundaries = None
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# ---------------------------
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# 需要修改的超参数
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# ---------------------------
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params_file = 'params_50_50_3'
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with open(params_file + '.yml', 'r', encoding='utf-8') as file:
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params = yaml.safe_load(file)
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H = params['H']
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W = params['W']
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k = params['num_cars']
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flight_time_factor = params['flight_time_factor']
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comp_time_factor = params['comp_time_factor']
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trans_time_factor = params['trans_time_factor']
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car_time_factor = params['car_time_factor']
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bs_time_factor = params['bs_time_factor']
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flight_energy_factor = params['flight_energy_factor']
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comp_energy_factor = params['comp_energy_factor']
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trans_energy_factor = params['trans_energy_factor']
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battery_energy_capacity = params['battery_energy_capacity']
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# # 定义数字列表
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# numbers = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9]
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row_cuts_set = [[0.3, 0.48, 0.77]]
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col_cuts_set = [[0.5]]
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for row_cuts in row_cuts_set:
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for col_cuts in col_cuts_set:
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row_boundaries = [0.0] + list(row_cuts) + [1.0]
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col_boundaries = [0.0] + list(col_cuts) + [1.0]
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# 这里面的距离不再是比例，而是真实距离！
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rectrangles = if_valid_partition(
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row_boundaries, col_boundaries, params)
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if not rectrangles:
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continue
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else:
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# 使用遗传算法求出每一种网格划分的可行解，然后选择其中的最优解
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current_solution, current_time, to_process_idx = GA_solver(
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rectrangles, params)
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if current_time < best_T:
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best_T = current_time
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best_solution = current_solution
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best_row_boundaries = row_boundaries
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best_col_boundaries = col_boundaries
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# 将best_solution分解成每个车队的路径
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found_start_points_indices = []
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for i in range(len(best_solution)):
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if best_solution[i] in to_process_idx:
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found_start_points_indices.append(i)
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car_paths = []
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for j in range(len(found_start_points_indices) - 1):
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from_index = found_start_points_indices[j]
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end_index = found_start_points_indices[j + 1]
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car_path = []
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for k in range(from_index, end_index + 1):
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rectrangle_idx = best_solution[k]
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if rectrangle_idx not in to_process_idx:
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car_path.append(rectrangle_idx - 1)
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if car_path:
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car_paths.append(car_path)
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# 输出最佳方案
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print("Best solution:", best_solution)
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print("Time:", best_T)
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print("Row boundaries:", best_row_boundaries)
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print("Col boundaries:", best_col_boundaries)
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print("Car Paths:", car_paths)
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@ -1,5 +1,6 @@
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import numpy as np
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from ga import GA
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import matplotlib.pyplot as plt
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def if_valid_partition(row_boundaries, col_boundaries, params):
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@ -91,4 +92,10 @@ def GA_solver(rectangles, params):
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if Best_path[-1] not in to_process_idx:
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Best_path.append(0)
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# iterations = model.iter_x
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# best_record = model.iter_y
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# plt.plot(iterations, best_record)
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# plt.show()
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return Best_path, Best, to_process_idx
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@ -73,37 +73,37 @@ class PartitionEnv(gym.Env):
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return state
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def step(self, action):
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# if action == 1:
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# self.row_cuts[1] += 0.01
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# elif action == 2:
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# self.row_cuts[1] -= 0.01
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# elif action == 3:
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# self.row_cuts[2] += 0.01
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# elif action == 4:
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# self.row_cuts[2] -= 0.01
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# elif action == 5:
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# self.row_cuts[3] += 0.01
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# elif action == 6:
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# self.row_cuts[3] -= 0.01
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# elif action == 7:
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# self.col_cuts[1] += 0.01
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# elif action == 8:
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# self.col_cuts[1] -= 0.01
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# elif action == 0:
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# pass
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cut_index, signal = (action + 1) // 2, (action + 1) % 2
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if action == 0:
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if action == 1:
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self.row_cuts[1] += 0.01
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elif action == 2:
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self.row_cuts[1] -= 0.01
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elif action == 3:
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self.row_cuts[2] += 0.01
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elif action == 4:
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self.row_cuts[2] -= 0.01
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elif action == 5:
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self.row_cuts[3] += 0.01
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elif action == 6:
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self.row_cuts[3] -= 0.01
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elif action == 7:
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self.col_cuts[1] += 0.01
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elif action == 8:
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self.col_cuts[1] -= 0.01
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elif action == 0:
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pass
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elif cut_index <= 5:
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if signal == 0:
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self.col_cuts[cut_index] += 0.005
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else:
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self.col_cuts[cut_index] -= 0.005
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else:
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if signal == 0:
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self.col_cuts[cut_index-4] += 0.005
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else:
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self.col_cuts[cut_index-4] -= 0.005
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# cut_index, signal = (action + 1) // 2, (action + 1) % 2
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# if action == 0:
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# pass
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# elif cut_index <= 5:
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# if signal == 0:
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# self.col_cuts[cut_index] += 0.005
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# else:
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# self.col_cuts[cut_index] -= 0.005
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# else:
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# if signal == 0:
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# self.col_cuts[cut_index-4] += 0.005
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# else:
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# self.col_cuts[cut_index-4] -= 0.005
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# 检查row_cuts和col_cuts是否按升序排列
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if (all(self.row_cuts[i] < self.row_cuts[i+1] for i in range(len(self.row_cuts)-1)) and
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@ -115,16 +115,16 @@ class PartitionEnv(gym.Env):
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# 不满足条件，时间给一个很大的值
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best_time = self.BASE_LINE * 2
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else:
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# # 满足条件，继续进行路径规划
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# # 每隔10步计算一次路径，第一次也需要计算路径，记录最佳路径
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# if self.adjust_step % 10 == 0 or self.adjust_step == 1 or self.best_path is None:
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# best_time, self.best_path = self.ga_solver(rectangles)
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# else:
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# # 根据最佳路径计算当前时间
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# best_time = self.get_best_time(self.best_path, rectangles)
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self.best_path = [33, 30, 29, 28, 27, 21, 15, 0, 13, 7, 1, 2, 31, 14, 8, 3, 4,
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10, 32, 23, 22, 24, 18, 17, 16, 35, 9, 12, 6, 5, 11, 34, 20, 25, 26, 19, 0]
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best_time = self.get_best_time(self.best_path, rectangles)
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# 满足条件，继续进行路径规划
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# 每隔10步计算一次路径，第一次也需要计算路径，记录最佳路径
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if self.adjust_step % 10 == 0 or self.best_path is None:
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best_time, self.best_path = self.ga_solver(rectangles)
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else:
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# 根据最佳路径计算当前时间
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best_time = self.get_best_time(self.best_path, rectangles)
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# self.best_path = [33, 30, 29, 28, 27, 21, 15, 0, 13, 7, 1, 2, 31, 14, 8, 3, 4,
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# 10, 32, 23, 22, 24, 18, 17, 16, 35, 9, 12, 6, 5, 11, 34, 20, 25, 26, 19, 0]
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# best_time = self.get_best_time(self.best_path, rectangles)
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else:
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# 调整不合法，时间给一个很大的值
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275
env_partion_dist1.py
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275
env_partion_dist1.py
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@ -0,0 +1,275 @@
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import gymnasium as gym
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from gymnasium import spaces
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import numpy as np
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import yaml
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import math
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from mTSP_solver import mTSP
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from GA.ga import GA
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class PartitionEnv(gym.Env):
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"""
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自定义环境，分为两阶段：
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区域切分，每一次切分都是(0, 1)之间的连续值
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"""
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def __init__(self, config=None):
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super(PartitionEnv, self).__init__()
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##############################
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# 可能需要手动修改的超参数
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##############################
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self.params = 'params_50_50_3'
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self.ORI_ROW_CUTS = [0, 0.1, 0.4, 0.7, 1]
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self.ORI_COL_CUTS = [0, 0.5, 1]
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self.CUT_NUM = 4
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self.BASE_LINE = 9051.163
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self.MAX_ADJUST_STEP = 50
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# self.ADJUST_THRESHOLD = 0.1
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# self.mTSP_STEPS = 10000
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# 切分位置+/-0.01
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self.action_space = spaces.Discrete(self.CUT_NUM*2 + 1)
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# 定义观察空间为8维向量
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self.observation_space = spaces.Box(
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low=0.0, high=1.0, shape=(len(self.ORI_ROW_CUTS)+len(self.ORI_COL_CUTS),), dtype=np.float32)
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self.row_cuts = self.ORI_ROW_CUTS[:]
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self.col_cuts = self.ORI_COL_CUTS[:]
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self.rectangles = []
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self.adjust_step = 0
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self.best_path = None
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# 车队参数设置
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with open(self.params + '.yml', 'r', encoding='utf-8') as file:
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params = yaml.safe_load(file)
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self.H = params['H']
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self.W = params['W']
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self.center = (self.H/2, self.W/2)
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self.num_cars = params['num_cars']
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self.flight_time_factor = params['flight_time_factor']
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self.comp_time_factor = params['comp_time_factor']
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self.trans_time_factor = params['trans_time_factor']
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self.car_time_factor = params['car_time_factor']
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self.bs_time_factor = params['bs_time_factor']
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self.flight_energy_factor = params['flight_energy_factor']
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self.comp_energy_factor = params['comp_energy_factor']
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self.trans_energy_factor = params['trans_energy_factor']
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self.battery_energy_capacity = params['battery_energy_capacity']
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def reset(self, seed=None, options=None):
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# 重置所有变量，回到切分阶段（phase 0）
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self.row_cuts = self.ORI_ROW_CUTS[:]
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self.col_cuts = self.ORI_COL_CUTS[:]
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self.rectangles = []
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self.adjust_step = 0
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self.best_path = None
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# 状态：前 4 维为 partition_values，其余为区域访问状态（初始全0）
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state = np.array(self.row_cuts + self.col_cuts)
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return state
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def step(self, action):
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if action == 1:
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self.row_cuts[1] += 0.01
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elif action == 2:
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self.row_cuts[1] -= 0.01
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elif action == 3:
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self.row_cuts[2] += 0.01
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elif action == 4:
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self.row_cuts[2] -= 0.01
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elif action == 5:
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self.row_cuts[3] += 0.01
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elif action == 6:
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self.row_cuts[3] -= 0.01
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elif action == 7:
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self.col_cuts[1] += 0.01
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elif action == 8:
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self.col_cuts[1] -= 0.01
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elif action == 0:
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pass
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# cut_index, signal = (action + 1) // 2, (action + 1) % 2
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# if action == 0:
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# pass
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# elif cut_index <= 5:
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# if signal == 0:
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# self.col_cuts[cut_index] += 0.005
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# else:
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# self.col_cuts[cut_index] -= 0.005
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# else:
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# if signal == 0:
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# self.col_cuts[cut_index-4] += 0.005
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# else:
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# self.col_cuts[cut_index-4] -= 0.005
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# 检查row_cuts和col_cuts是否按升序排列
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if (all(self.row_cuts[i] < self.row_cuts[i+1] for i in range(len(self.row_cuts)-1)) and
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all(self.col_cuts[i] < self.col_cuts[i+1] for i in range(len(self.col_cuts)-1))):
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# 调整是合法的，验证分区情况是否满足条件
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rectangles = self.if_valid_partition()
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if not rectangles:
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# 不满足条件，时间给一个很大的值
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best_time = self.BASE_LINE * 2
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else:
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# 满足条件，继续进行路径规划
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# 每隔10步计算一次路径，第一次也需要计算路径，记录最佳路径
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if self.adjust_step % 10 == 0 or self.best_path is None:
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best_time, self.best_path = self.ga_solver(rectangles)
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else:
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# 根据最佳路径计算当前时间
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best_time = self.get_best_time(self.best_path, rectangles)
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# self.best_path = [33, 30, 29, 28, 27, 21, 15, 0, 13, 7, 1, 2, 31, 14, 8, 3, 4,
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# 10, 32, 23, 22, 24, 18, 17, 16, 35, 9, 12, 6, 5, 11, 34, 20, 25, 26, 19, 0]
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# best_time = self.get_best_time(self.best_path, rectangles)
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else:
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# 调整不合法，时间给一个很大的值
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best_time = self.BASE_LINE * 2
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reward = self.calc_reward(best_time)
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self.adjust_step += 1
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state = np.array(self.row_cuts + self.col_cuts)
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info = {'row_cuts': self.row_cuts, 'col_cuts': self.col_cuts,
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'best_path': self.best_path, 'best_time': best_time}
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if self.adjust_step < self.MAX_ADJUST_STEP:
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return state, reward, False, False, info
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else:
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return state, reward, True, False, info
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def if_valid_partition(self):
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rectangles = []
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for i in range(len(self.row_cuts) - 1):
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for j in range(len(self.col_cuts) - 1):
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d = (self.col_cuts[j+1] - self.col_cuts[j]) * self.W * \
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(self.row_cuts[i+1] -
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self.row_cuts[i]) * self.H
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rho_time_limit = (self.flight_time_factor - self.trans_time_factor) / \
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(self.comp_time_factor - self.trans_time_factor)
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rho_energy_limit = (self.battery_energy_capacity - self.flight_energy_factor * d - self.trans_energy_factor * d) / \
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(self.comp_energy_factor * d -
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self.trans_energy_factor * d)
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if rho_energy_limit < 0:
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return []
|
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rho = min(rho_time_limit, rho_energy_limit)
|
||||
|
||||
flight_time = self.flight_time_factor * d
|
||||
bs_time = self.bs_time_factor * (1 - rho) * d
|
||||
|
||||
rectangles.append({
|
||||
'center': ((self.row_cuts[i] + self.row_cuts[i+1]) * self.H / 2, (self.col_cuts[j+1] + self.col_cuts[j]) * self.W / 2),
|
||||
'flight_time': flight_time,
|
||||
'bs_time': bs_time,
|
||||
})
|
||||
return rectangles
|
||||
|
||||
def check_adjustment_threshold(self, threshold=0.1):
|
||||
"""
|
||||
检查当前切分位置与原始切分位置的差异是否超过阈值
|
||||
Args:
|
||||
threshold (float): 允许的最大调整幅度
|
||||
Returns:
|
||||
bool: 如果任何切分位置的调整超过阈值，返回True
|
||||
"""
|
||||
# 检查行切分位置
|
||||
delta = 0
|
||||
for i in range(len(self.row_cuts)):
|
||||
delta += abs(self.row_cuts[i] - self.ORI_ROW_CUTS[i])
|
||||
|
||||
# 检查列切分位置
|
||||
for i in range(len(self.col_cuts)):
|
||||
delta += abs(self.col_cuts[i] - self.ORI_COL_CUTS[i])
|
||||
|
||||
if delta > threshold:
|
||||
return True
|
||||
|
||||
return False
|
||||
|
||||
# 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 = []
|
||||
# for i in range(self.num_cars - 1):
|
||||
# cities = np.column_stack((cities, self.center))
|
||||
# center_idx.append(cities.shape[1] - 1)
|
||||
|
||||
# tsp = mTSP(params=self.params, num_cities=cities.shape[1], cities=cities, num_cars=self.num_cars,
|
||||
# center_idx=center_idx, rectangles=rectangles)
|
||||
|
||||
# best_time, best_path = tsp.train(self.mTSP_STEPS)
|
||||
|
||||
def ga_solver(self, rectangles):
|
||||
cities = [self.center]
|
||||
for rec in rectangles:
|
||||
cities.append(rec['center'])
|
||||
cities = np.array(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)
|
||||
|
||||
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 get_best_time(self, best_path, rectangles):
|
||||
cities = [self.center]
|
||||
for rec in rectangles:
|
||||
cities.append(rec['center'])
|
||||
cities = np.array(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)
|
||||
|
||||
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_time = ga.compute_pathlen(best_path)
|
||||
return best_time
|
||||
|
||||
def calc_reward(self, best_time):
|
||||
"""
|
||||
计算奖励：
|
||||
1. 如果时间小于基准线，给予正奖励
|
||||
2. 如果时间大于基准线，给予负奖励
|
||||
3. 保持归一化和折扣因子
|
||||
|
||||
Args:
|
||||
best_time (float): 当前路径的时间
|
||||
Returns:
|
||||
float: 计算得到的奖励值
|
||||
"""
|
||||
time_diff = self.BASE_LINE - best_time
|
||||
|
||||
# 使用tanh归一化，确保time_diff=0时，normalized_diff=0
|
||||
# tanh在变量值为2时，就非常接近1了。最大的time_diff为400
|
||||
normalized_diff = np.tanh(time_diff / 200) # 20是缩放因子，可调整
|
||||
|
||||
# 计算最终奖励
|
||||
reward = normalized_diff
|
||||
# * step_weight # 10是缩放因子
|
||||
|
||||
return reward
|
||||
|
||||
def render(self):
|
||||
if self.phase == 1:
|
||||
print("Phase 1: Initialize maze environment.")
|
||||
print(f"Partition values so far: {self.partition_values}")
|
||||
print(f"Motorcade positon: {self.car_pos}")
|
||||
# input('1111')
|
||||
elif self.phase == 2:
|
||||
print("Phase 2: Play maze.")
|
||||
print(f'Motorcade trajectory: {self.car_traj}')
|
||||
# input('2222')
|
||||
@ -1,6 +1,6 @@
|
||||
# from env import PartitionMazeEnv
|
||||
# from env_dis import PartitionMazeEnv
|
||||
from env_partion_dist import PartitionEnv
|
||||
from env_partion_dist1 import PartitionEnv
|
||||
|
||||
# env = PartitionMazeEnv()
|
||||
env = PartitionEnv()
|
||||
@ -13,7 +13,7 @@ print('state:', state)
|
||||
# action_series = [1] * 30
|
||||
# action_series = [[0.2], [0.4], [0.7], [0.5]]
|
||||
# action_series = [[-0.08], [-0.08], [0], [0]]
|
||||
action_series = list(range(11))
|
||||
action_series = list(range(9))
|
||||
|
||||
for i in range(100):
|
||||
action = action_series[i]
|
||||
|
||||
@ -1,60 +0,0 @@
|
||||
{
|
||||
"best_time": 19376.05694186515,
|
||||
"row_cuts": [
|
||||
0,
|
||||
0.2800000000000001,
|
||||
0.43000000000000005,
|
||||
0.62,
|
||||
0.77,
|
||||
1
|
||||
],
|
||||
"col_cuts": [
|
||||
0,
|
||||
0.2,
|
||||
0.4,
|
||||
0.5,
|
||||
0.7,
|
||||
0.8,
|
||||
1
|
||||
],
|
||||
"best_path": [
|
||||
33,
|
||||
30,
|
||||
29,
|
||||
28,
|
||||
27,
|
||||
21,
|
||||
15,
|
||||
0,
|
||||
13,
|
||||
7,
|
||||
1,
|
||||
2,
|
||||
31,
|
||||
14,
|
||||
8,
|
||||
3,
|
||||
4,
|
||||
10,
|
||||
32,
|
||||
23,
|
||||
22,
|
||||
24,
|
||||
18,
|
||||
17,
|
||||
16,
|
||||
35,
|
||||
9,
|
||||
12,
|
||||
6,
|
||||
5,
|
||||
11,
|
||||
34,
|
||||
20,
|
||||
25,
|
||||
26,
|
||||
19,
|
||||
0
|
||||
],
|
||||
"timestamp": "2025-04-06 09:10:53"
|
||||
}
|
||||
@ -1,30 +0,0 @@
|
||||
{
|
||||
"best_time": 8820.015746422654,
|
||||
"row_cuts": [
|
||||
0,
|
||||
0.2900000000000001,
|
||||
0.4700000000000001,
|
||||
0.77,
|
||||
1
|
||||
],
|
||||
"col_cuts": [
|
||||
0,
|
||||
0.5,
|
||||
1
|
||||
],
|
||||
"best_path": [
|
||||
0,
|
||||
1,
|
||||
3,
|
||||
5,
|
||||
9,
|
||||
7,
|
||||
8,
|
||||
10,
|
||||
2,
|
||||
4,
|
||||
6,
|
||||
0
|
||||
],
|
||||
"timestamp": "2025-04-03 10:58:44"
|
||||
}
|
||||
@ -199,8 +199,8 @@ if __name__ == "__main__":
|
||||
# ---------------------------
|
||||
# 需要修改的超参数
|
||||
# ---------------------------
|
||||
params_file = 'params_100_100_6'
|
||||
solution_file = r'solutions\finetune_params_100_100_6.json'
|
||||
params_file = 'params_50_50_3'
|
||||
solution_file = r'solutions\dqn_params_100_100_6.json'
|
||||
|
||||
with open(params_file + '.yml', 'r', encoding='utf-8') as file:
|
||||
params = yaml.safe_load(file)
|
||||
|
||||
Loading…
Reference in New Issue
Block a user