296 lines
14 KiB
Python
296 lines
14 KiB
Python
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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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class PartitionMazeEnv(gym.Env):
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"""
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自定义环境，分为两阶段：
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阶段 0：区域切分（共 4 步，每一步输出一个标量，用于确定竖切和横切位置）。
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切分顺序为：第一步输出 c₁，第二步输出 c₂，第三步输出 r₁，第四步输出 r₂。
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离散化后取值仅为 {0, 0.1, 0.2, …, 0.9}（其中 0 表示不切）。
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阶段 1：车辆路径规划（走迷宫），车辆从区域中心出发，在九宫格内按照上下左右移动，
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直到所有目标格子被覆盖或步数上限达到。
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"""
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def __init__(self, config=None):
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super(PartitionMazeEnv, self).__init__()
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# 车队参数设置
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self.H = 20 # 区域高度，网格点之间的距离为25m（单位距离）
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self.W = 30 # 区域宽度
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self.num_cars = 2 # 系统数量（车-巢-机系统个数）
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# 时间系数（单位：秒，每个网格一张照片）
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self.flight_time_factor = 3 # 每张照片对应的飞行时间，无人机飞行速度为9.5m/s，拍摄照片的时间间隔为3s
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self.comp_uav_factor = 5 # 无人机上每张照片计算时间，5s
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self.trans_time_factor = 0.3 # 每张照片传输时间，0.3s
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self.car_move_time_factor = 2 * 50 # TODO 汽车每单位距离的移动时间，2s，加了一个放大因子
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self.comp_bs_factor = 5 # 机巢上每张照片计算时间
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# 能耗参数
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self.flight_energy_factor = 0.05 # 单位：分钟/张
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self.comp_energy_factor = 0.05 # 计算能耗需要重新估计
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self.trans_energy_factor = 0.0025
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self.battery_capacity = 10 # 无人机只进行飞行，续航为30分钟
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self.phase = 0 # 阶段控制，0：区域划分阶段，1：迷宫初始化阶段，2：走迷宫阶段
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self.partition_step = 0 # 区域划分阶段步数，范围 0~4
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# TODO 切的刀数现在固定为4（2+2）
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self.partition_values = np.zeros(
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4, dtype=np.float32) # 存储 c₁, c₂, r₁, r₂
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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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# TODO 返回的状态目前只有位置坐标
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# 阶段 0 状态：前 4 维表示已决策的切分值（未决策部分为 0）
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# 阶段 1 状态：车辆位置 (2D)
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self.observation_space = spaces.Box(
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low=0.0, high=1.0, shape=(8,), dtype=np.float32)
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# 切分阶段相关变量
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self.vertical_cuts = [] # 存储竖切位置（c₁, c₂），当值为0时表示不切
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self.horizontal_cuts = [] # 存储横切位置（r₁, r₂）
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# TODO region_centers可不可以优化一下，减少一些参数
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self.region_centers = [] # 存储切分后每个子区域的中心点（归一化坐标）
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# 路径规划阶段相关变量
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self.MAX_STEPS = 50 # 迷宫走法步数上限
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self.step_count = 0
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self.rectangles = {}
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self.car_pos = [[0.5, 0.5] for _ in range(self.num_cars)]
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self.car_traj = [[] for _ in range(self.num_cars)]
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self.current_car_index = 0
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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.partition_values = np.zeros(4, dtype=np.float32)
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self.vertical_cuts = []
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self.horizontal_cuts = []
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self.region_centers = []
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self.step_count = 0
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self.rectangles = {}
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self.car_pos = [[0.5, 0.5] for _ in range(self.num_cars)]
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self.car_traj = [[] for _ in range(self.num_cars)]
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self.current_car_index = 0
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# 状态：前 4 维为 partition_values，其余补 0
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state = np.concatenate(
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[self.partition_values, np.zeros(np.array(self.car_pos).flatten().shape[0], dtype=np.float32)])
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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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a = float(action[0])
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if self.phase == 0:
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# 切分阶段：每一步输出一个标量，离散化为 {0, 0.1, ..., 0.9}
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disc_val = np.floor(a * 10) / 10.0
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disc_val = np.clip(disc_val, 0.0, 0.9)
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self.partition_values[self.partition_step] = disc_val
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self.partition_step += 1
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# 构造当前状态：前 partition_step 个为已决策值，其余为 0，再补 7 个 0
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state = np.concatenate(
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[self.partition_values, np.zeros(np.array(self.car_pos).flatten().shape[0], dtype=np.float32)])
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# 如果未完成 4 步，则仍处于切分阶段，不发奖励，done 为 False
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if self.partition_step < 4:
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return state, 0.0, False, False, {}
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else:
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# 完成 4 步后，计算切分边界
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# 过滤掉 0，并去重后排序
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vert = sorted(set(v for v in self.partition_values[:len(
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self.partition_values) // 2] if v > 0))
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horiz = sorted(set(v for v in self.partition_values[len(
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self.partition_values) // 2:] if v > 0))
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self.vertical_cuts = vert if vert else []
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self.horizontal_cuts = horiz if horiz else []
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# 边界：始终包含 0 和 1
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v_boundaries = [0.0] + self.vertical_cuts + [1.0]
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h_boundaries = [0.0] + self.horizontal_cuts + [1.0]
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# 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(h_boundaries) - 1):
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for j in range(len(v_boundaries) - 1):
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d = (v_boundaries[j+1] - v_boundaries[j]) * self.W * \
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(h_boundaries[i] + h_boundaries[i+1]) * self.H
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rho_time_limit = (self.flight_time_factor - self.trans_time_factor) / \
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(self.comp_uav_factor - self.trans_time_factor)
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rho_energy_limit = (self.battery_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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comp_time = self.comp_uav_factor * rho * d
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trans_time = self.trans_time_factor * (1 - rho) * d
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comp_bs_time = self.comp_bs_factor * (1 - rho) * d
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self.rectangles[(i, j)] = {
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# 'r1': h_boundaries[i], 'r2': h_boundaries[i+1], 'c1': v_boundaries[j], 'c2': v_boundaries[j+1],
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'd': d,
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'rho': rho,
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'flight_time': flight_time,
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'comp_time': comp_time,
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'trans_time': trans_time,
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'comp_bs_time': comp_bs_time,
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'is_visited': False
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# 'center': (center_r, center_c)
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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 = -100
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state = np.concatenate(
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[self.partition_values, np.zeros(np.array(self.car_pos).flatten().shape[0], dtype=np.float32)])
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return state, reward, True, False, {}
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else:
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reward = 10
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# 进入阶段 1：走迷宫
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self.phase = 1
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# 根据分割边界计算每个子区域中心
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self.region_centers = []
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for i in range(len(h_boundaries) - 1):
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for j in range(len(v_boundaries) - 1):
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center_x = (
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v_boundaries[j] + v_boundaries[j+1]) / 2.0
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center_y = (
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h_boundaries[i] + h_boundaries[i+1]) / 2.0
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self.region_centers.append((center_x, center_y))
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# 存储切分边界，供后续网格映射使用
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self.v_boundaries = v_boundaries
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self.h_boundaries = h_boundaries
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# 初始化迷宫阶段：步数清零，建立 visited_grid 大小与网格数相同
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self.step_count = 0
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self.visited_grid = np.zeros(
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(len(v_boundaries) - 1) * (len(h_boundaries) - 1), dtype=np.int32)
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state = np.concatenate(
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[self.partition_values, np.array(self.car_pos).flatten()])
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return state, reward, False, False, {}
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elif self.phase == 1:
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# 阶段 1：初始化迷宫，让多个车辆从区域中心出发，前往划分区域的中心点
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# 确保 action 的值在 [0, 1]，然后映射到 0~(num_regions-1) 的索引
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num_regions = len(self.region_centers)
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target_region_index = int(np.floor(a * num_regions))
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target_region_index = np.clip(
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target_region_index, 0, num_regions - 1)
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# 遍历所有车辆，让它们依次移动到目标子区域
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for car_idx in range(self.num_cars):
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target_position = np.array(
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self.region_centers[target_region_index]) # 目标区域中心
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# 更新该车辆位置
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self.car_pos[car_idx] = target_position
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# 累计步数
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self.step_count += 1
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self.car_traj[car_idx].append(target_position) # 记录每辆车的轨迹
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# 进入阶段 2：走迷宫
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self.phase = 2
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# 观察状态
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state = np.concatenate(
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[self.partition_values, np.array(self.car_pos).flatten()])
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return state, 0.0, False, False, {}
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elif self.phase == 2:
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# 阶段 2：路径规划（走迷宫）
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current_car = self.current_car_index
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# 当前动作 a 为 1 维连续动作，映射到四个方向
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if a < 0.2:
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move_dir = 'up'
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elif a < 0.4:
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move_dir = 'down'
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elif a < 0.6:
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move_dir = 'left'
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elif a < 0.8:
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move_dir = 'right'
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else:
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move_dir = 'stay'
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current_row, current_col = self.car_pos[current_car]
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# 初始化新的行、列为当前值
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new_row, new_col = current_row, current_col
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if move_dir == 'up' and current_row < len(h_boundaries) - 1:
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new_row = current_row + 1
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elif move_dir == 'down' and current_row > 0:
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new_row = current_row - 1
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elif move_dir == 'left' and current_col > 0:
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new_col = current_col - 1
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elif move_dir == 'right' and current_col < len(v_boundaries) - 1:
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new_col = current_col + 1
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# 如果移动不合法，或者动作为stay，则保持原位置
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# TODO 移动不合法，加一些惩罚
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# 更新车辆位置
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self.car_pos[current_car] = [new_row, new_col]
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if new_row != current_row or new_col != current_col:
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self.car_traj[current_car].append(np.array(new_row, new_col))
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self.step_count += 1
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self.current_car_index = (
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self.current_car_index + 1) % self.num_cars
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# 更新访问标记：将新网格标记为已访问
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self.rectangles[(new_col, new_col)]['is_visited'] = True
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# 观察状态
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state = np.concatenate(
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[self.partition_values, np.array(self.car_pos).flatten()])
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# Episode 终止条件：所有网格均被访问或步数达到上限
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done = all([rec['is_visited'] for rec in self.rectangles]) or (
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self.step_count >= self.MAX_STEPS)
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if done and np.all(self.visited_grid == 1):
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# 区域覆盖完毕，根据轨迹计算各车队的执行时间
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T = max([self._compute_motorcade_time(idx)
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for idx in range(self.num_cars)])
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reward += 10.0 # TODO 奖励与greedy比较
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elif done and self.step_count >= self.MAX_STEPS:
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reward -= 100
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return state, reward, done, False, {}
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def _compute_motorcade_time(self, idx):
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flight_time = sum(self.rectangles[point]['flight_time']
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for point in self.car_traj[idx])
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bs_time = sum(self.rectangles[point]['comp_bs_time']
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for point in self.car_traj[idx])
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# 计算车的移动时间，首先在轨迹的首尾添加上大区域中心
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self.car_traj[idx].append([0.5, 0.5])
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|||
|
|
self.car_traj[idx].insert(0, [0.5, 0.5])
|
|||
|
|
for i in range(len(self.car_traj[idx])):
|
|||
|
|
first_point = self.car_traj[idx][i]
|
|||
|
|
second_point = self.car_traj[idx][i + 1]
|
|||
|
|
car_time += np.linalg.norm(first_point, second_point) * \
|
|||
|
|
self.H * self.W * self.car_move_time_factor
|
|||
|
|
|
|||
|
|
return max(car_time + flight_time, bs_time)
|
|||
|
|
|
|||
|
|
def render(self):
|
|||
|
|
if self.phase == 0:
|
|||
|
|
print("Phase 0: Partitioning.")
|
|||
|
|
print(f"Partition step: {self.partition_step}")
|
|||
|
|
print(f"Partition values so far: {self.partition_values}")
|
|||
|
|
elif self.phase == 1:
|
|||
|
|
print("Phase 1: Path planning (maze).")
|
|||
|
|
print(f"Visited grid: {self.visited_grid}")
|
|||
|
|
print(f"Step count: {self.step_count}")
|