198 lines
8.0 KiB
Python
198 lines
8.0 KiB
Python
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 = 'params2'
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self.ORI_ROW_CUTS = [0, 0.2, 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.ROW_CUT_LIMIT = 3
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self.COL_CUT_LIMIT = 1
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self.BASE_LINE = 10000
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self.mTSP_STEPS = 10000
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# 定义动作空间：全部动作均为 1 维连续 [0,1]
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self.action_space = spaces.Box(
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low=0.0, high=1.0, shape=(1,), dtype=np.float32)
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# 定义观察空间为8维向量
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# 前 4 维表示已决策的切分值（未决策部分为 0）
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self.observation_space = spaces.Box(
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low=0.0, high=1.0, shape=(self.CUT_NUM + 4,), dtype=np.float32)
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self.partition_step = 0
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self.ori_row_cuts = self.ORI_ROW_CUTS[:]
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self.ori_col_cuts = self.ORI_COL_CUTS[:]
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self.rectangles = []
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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.phase = 0
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self.partition_step = 0
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self.ori_row_cuts = self.ORI_ROW_CUTS[:]
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self.ori_col_cuts = self.ORI_COL_CUTS[:]
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self.rectangles = []
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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.ori_row_cuts + self.ori_col_cuts)
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return state
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def step(self, action):
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# 在所有阶段动作均为 1 维连续动作，取 action[0]
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adjust = float(action[0])
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valid_adjust = True
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if self.partition_step < self.ROW_CUT_LIMIT:
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row_cut = self.ori_row_cuts[self.partition_step + 1]
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new_row_cut = row_cut + adjust
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self.ori_row_cuts[self.partition_step + 1] = new_row_cut
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if self.ori_row_cuts[self.partition_step] < new_row_cut < self.ori_row_cuts[self.partition_step + 2]:
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pass
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else:
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valid_adjust = False
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reward = -100
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else:
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col_idx = self.partition_step - self.ROW_CUT_LIMIT
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col_cut = self.ori_col_cuts[col_idx + 1]
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new_col_cut = col_cut + adjust
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self.ori_col_cuts[col_idx + 1] = new_col_cut
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if self.ori_col_cuts[col_idx] < new_col_cut < self.ori_col_cuts[col_idx + 2]:
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pass
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else:
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valid_adjust = False
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reward = -100
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self.partition_step += 1
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state = np.array(self.ori_row_cuts + self.ori_col_cuts)
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# 出现无效调整，直接结束
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if not valid_adjust:
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return state, reward, True, False, {}
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else:
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if self.partition_step < self.CUT_NUM:
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return state, 0.0, False, False, {}
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else:
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# 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
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valid_partition = True
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for i in range(len(self.ori_row_cuts) - 1):
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for j in range(len(self.ori_col_cuts) - 1):
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d = (self.ori_col_cuts[j+1] - self.ori_col_cuts[j]) * self.W * \
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(self.ori_row_cuts[i+1] -
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self.ori_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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valid_partition = False
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break
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rho = min(rho_time_limit, rho_energy_limit)
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flight_time = self.flight_time_factor * d
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bs_time = self.bs_time_factor * (1 - rho) * d
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self.rectangles.append({
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'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),
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'flight_time': flight_time,
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'bs_time': bs_time,
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})
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if not valid_partition:
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break
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if not valid_partition:
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reward = -10
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return state, reward, True, False, {}
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else:
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# 继续进行路径规划
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# 使用q_learning解多旅行商
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# cities: [[x1, x2, x3...], [y1, y2, y3...]] 城市坐标
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# rec_center_lt = [rec_info['center']
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# for rec_info in self.rectangles]
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# cities = np.column_stack(rec_center_lt)
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# cities = np.column_stack((self.center, cities))
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# center_idx = []
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# for i in range(self.num_cars - 1):
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# cities = np.column_stack((cities, self.center))
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# center_idx.append(cities.shape[1] - 1)
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# tsp = mTSP(params=self.params, num_cities=cities.shape[1], cities=cities, num_cars=self.num_cars,
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# center_idx=center_idx, rectangles=self.rectangles)
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# best_time, best_path = tsp.train(self.mTSP_STEPS)
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# 使用遗传算法解多旅行商
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cities = [self.center]
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for rec in self.rectangles:
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cities.append(rec['center'])
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cities = np.array(cities)
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center_idx = [0]
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for i in range(self.num_cars - 1):
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cities = np.row_stack((cities, self.center))
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center_idx.append(cities.shape[0] - 1)
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ga = GA(num_drones=self.num_cars, num_city=cities.shape[0], num_total=20,
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data=cities, to_process_idx=center_idx, rectangles=self.rectangles)
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best_path, best_time = ga.run()
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# print(best_time)
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# print(best_path)
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reward = self.BASE_LINE / best_time
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return state, reward, True, False, best_path
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def render(self):
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if self.phase == 1:
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print("Phase 1: Initialize maze environment.")
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print(f"Partition values so far: {self.partition_values}")
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print(f"Motorcade positon: {self.car_pos}")
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# input('1111')
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elif self.phase == 2:
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print("Phase 2: Play maze.")
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print(f'Motorcade trajectory: {self.car_traj}')
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# input('2222')
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