276 lines
10 KiB
Python
276 lines
10 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 = 'params_100_100_6'
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self.ORI_ROW_CUTS = [0, 0.28, 0.43, 0.62, 0.77, 1]
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self.ORI_COL_CUTS = [0, 0.2, 0.4, 0.5, 0.7, 0.8, 1]
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self.CUT_NUM = 5
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self.BASE_LINE = 19376.06
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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)
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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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rectangles.append({
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'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),
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'flight_time': flight_time,
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'bs_time': bs_time,
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})
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return rectangles
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def check_adjustment_threshold(self, threshold=0.1):
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"""
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检查当前切分位置与原始切分位置的差异是否超过阈值
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Args:
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threshold (float): 允许的最大调整幅度
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Returns:
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bool: 如果任何切分位置的调整超过阈值，返回True
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"""
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# 检查行切分位置
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delta = 0
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for i in range(len(self.row_cuts)):
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delta += abs(self.row_cuts[i] - self.ORI_ROW_CUTS[i])
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# 检查列切分位置
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for i in range(len(self.col_cuts)):
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delta += abs(self.col_cuts[i] - self.ORI_COL_CUTS[i])
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if delta > threshold:
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return True
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return False
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# def q_learning_solver(self):
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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 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=rectangles)
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# best_time, best_path = tsp.train(self.mTSP_STEPS)
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def ga_solver(self, rectangles):
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cities = [self.center]
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for rec in 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=rectangles)
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best_path, best_time = ga.run()
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return best_time, best_path
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def get_best_time(self, best_path, rectangles):
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cities = [self.center]
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for rec in 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=rectangles)
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best_time = ga.compute_pathlen(best_path)
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return best_time
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def calc_reward(self, best_time):
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"""
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计算奖励：
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1. 如果时间小于基准线，给予正奖励
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2. 如果时间大于基准线，给予负奖励
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3. 保持归一化和折扣因子
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Args:
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best_time (float): 当前路径的时间
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Returns:
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float: 计算得到的奖励值
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"""
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time_diff = self.BASE_LINE - best_time
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# 使用tanh归一化，确保time_diff=0时，normalized_diff=0
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# tanh在变量值为2时，就非常接近1了。最大的time_diff为400
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normalized_diff = np.tanh(time_diff / 5000) # 20是缩放因子，可调整
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# 计算最终奖励
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reward = normalized_diff
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# * step_weight # 10是缩放因子
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return reward
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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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