From 3818343085f669fc21d6d80118925eaa0eefcca8 Mon Sep 17 00:00:00 2001 From: weixin_46229132 Date: Tue, 11 Mar 2025 19:43:04 +0800 Subject: [PATCH] =?UTF-8?q?PPO=E8=83=BD=E5=A4=9F=E8=B7=91=E8=B5=B7?= =?UTF-8?q?=E6=9D=A5=E4=BA=86?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- PPO/env.py | 138 +++++++++++++++++++++++------------------------------ 1 file changed, 60 insertions(+), 78 deletions(-) diff --git a/PPO/env.py b/PPO/env.py index 51e2e9e..9e124e6 100644 --- a/PPO/env.py +++ b/PPO/env.py @@ -1,6 +1,8 @@ import gymnasium as gym from gymnasium import spaces import numpy as np +import yaml +import math class PartitionMazeEnv(gym.Env): @@ -16,22 +18,23 @@ class PartitionMazeEnv(gym.Env): def __init__(self, config=None): super(PartitionMazeEnv, self).__init__() # 车队参数设置 - self.H = 20 # 区域高度，网格点之间的距离为25m（单位距离） - self.W = 30 # 区域宽度 - self.num_cars = 2 # 系统数量（车-巢-机系统个数） + with open('params.yml', 'r', encoding='utf-8') as file: + params = yaml.safe_load(file) - # 时间系数（单位：秒，每个网格一张照片） - 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 - self.comp_uav_factor = 5 # 无人机上每张照片计算时间，5s - self.trans_time_factor = 0.3 # 每张照片传输时间，0.3s - 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 - self.comp_bs_factor = 5 # 机巢上每张照片计算时间 + self.H = params['H'] + self.W = params['W'] + self.num_cars = params['num_cars'] - # 能耗参数 - self.flight_energy_factor = 0.05 # 单位：分钟/张 - self.comp_energy_factor = 0.05 # 计算能耗需要重新估计 - self.trans_energy_factor = 0.0025 - self.battery_capacity = 10 # 无人机只进行飞行，续航为30分钟 + self.flight_time_factor = params['flight_time_factor'] + self.comp_time_factor = params['comp_time_factor'] + self.trans_time_factor = params['trans_time_factor'] + self.car_time_factor = params['car_time_factor'] + self.bs_time_factor = params['bs_time_factor'] + + self.flight_energy_factor = params['flight_energy_factor'] + self.comp_energy_factor = params['comp_energy_factor'] + self.trans_energy_factor = params['trans_energy_factor'] + self.battery_energy_capacity = params['battery_energy_capacity'] 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 self.partition_step = 0 # 区域划分阶段步数，范围 0~4 @@ -48,16 +51,17 @@ class PartitionMazeEnv(gym.Env): # 阶段 0 状态：前 4 维表示已决策的切分值（未决策部分为 0） # 阶段 1 状态：车辆位置 (2D) self.observation_space = spaces.Box( - low=0.0, high=1.0, shape=(8,), dtype=np.float32) + low=0.0, high=1.0, shape=(4 + 2 * self.num_cars,), dtype=np.float32) # 切分阶段相关变量 self.vertical_cuts = [] # 存储竖切位置（c₁, c₂），当值为0时表示不切 self.horizontal_cuts = [] # 存储横切位置（r₁, r₂） - # TODO region_centers可不可以优化一下，减少一些参数 - self.region_centers = [] # 存储切分后每个子区域的中心点（归一化坐标） + + self.init_maze_step = 0 # 路径规划阶段相关变量 self.MAX_STEPS = 50 # 迷宫走法步数上限 + self.BASE_LINE = 2750.0 # 基准时间，通过greedy或者蒙特卡洛计算出来 self.step_count = 0 self.rectangles = {} self.car_pos = [[0.5, 0.5] for _ in range(self.num_cars)] @@ -71,6 +75,7 @@ class PartitionMazeEnv(gym.Env): self.partition_values = np.zeros(4, dtype=np.float32) self.vertical_cuts = [] self.horizontal_cuts = [] + self.init_maze_step = 0 self.region_centers = [] self.step_count = 0 self.rectangles = {} @@ -121,8 +126,8 @@ class PartitionMazeEnv(gym.Env): d = (v_boundaries[j+1] - v_boundaries[j]) * self.W * \ (h_boundaries[i] + h_boundaries[i+1]) * self.H rho_time_limit = (self.flight_time_factor - self.trans_time_factor) / \ - (self.comp_uav_factor - self.trans_time_factor) - rho_energy_limit = (self.battery_capacity - self.flight_energy_factor * d - self.trans_energy_factor * d) / \ + (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: @@ -131,20 +136,12 @@ class PartitionMazeEnv(gym.Env): rho = min(rho_time_limit, rho_energy_limit) flight_time = self.flight_time_factor * d - comp_time = self.comp_uav_factor * rho * d - trans_time = self.trans_time_factor * (1 - rho) * d - comp_bs_time = self.comp_bs_factor * (1 - rho) * d + bs_time = self.bs_time_factor * (1 - rho) * d self.rectangles[(i, j)] = { - # 'r1': h_boundaries[i], 'r2': h_boundaries[i+1], 'c1': v_boundaries[j], 'c2': v_boundaries[j+1], - 'd': d, - 'rho': rho, 'flight_time': flight_time, - 'comp_time': comp_time, - 'trans_time': trans_time, - 'comp_bs_time': comp_bs_time, + 'bs_time': bs_time, 'is_visited': False - # 'center': (center_r, center_c) } if not valid_partition: break @@ -157,25 +154,11 @@ class PartitionMazeEnv(gym.Env): else: reward = 10 - # 进入阶段 1：走迷宫 + # 进入阶段 1：初始化迷宫 self.phase = 1 - # 根据分割边界计算每个子区域中心 - self.region_centers = [] - for i in range(len(h_boundaries) - 1): - for j in range(len(v_boundaries) - 1): - center_x = ( - v_boundaries[j] + v_boundaries[j+1]) / 2.0 - center_y = ( - h_boundaries[i] + h_boundaries[i+1]) / 2.0 - self.region_centers.append((center_x, center_y)) # 存储切分边界，供后续网格映射使用 self.v_boundaries = v_boundaries self.h_boundaries = h_boundaries - # 初始化迷宫阶段：步数清零，建立 visited_grid 大小与网格数相同 - self.step_count = 0 - self.visited_grid = np.zeros( - (len(v_boundaries) - 1) * (len(h_boundaries) - 1), dtype=np.int32) - state = np.concatenate( [self.partition_values, np.array(self.car_pos).flatten()]) return state, reward, False, False, {} @@ -183,33 +166,33 @@ class PartitionMazeEnv(gym.Env): elif self.phase == 1: # 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 # 确保 action 的值在 [0, 1]，然后映射到 0~(num_regions-1) 的索引 - num_regions = len(self.region_centers) + num_regions = (len(self.v_boundaries) - 1) * \ + (len(self.h_boundaries) - 1) target_region_index = int(np.floor(a * num_regions)) target_region_index = np.clip( target_region_index, 0, num_regions - 1) + # 将index映射到笛卡尔坐标 + coord = [target_region_index // (len(self.v_boundaries) - 1), + target_region_index % (len(self.v_boundaries) - 1)] + self.car_pos[self.init_maze_step] = coord + self.car_traj[self.init_maze_step].append(coord) + self.rectangles[tuple(coord)]['is_visited'] = True - # 遍历所有车辆，让它们依次移动到目标子区域 - for car_idx in range(self.num_cars): - target_position = np.array( - self.region_centers[target_region_index]) # 目标区域中心 - - # 更新该车辆位置 - self.car_pos[car_idx] = target_position - # 累计步数 - self.step_count += 1 - self.car_traj[car_idx].append(target_position) # 记录每辆车的轨迹 - - # 进入阶段 2：走迷宫 - self.phase = 2 - - # 观察状态 + # 计数 + self.init_maze_step += 1 state = np.concatenate( [self.partition_values, np.array(self.car_pos).flatten()]) - return state, 0.0, False, False, {} + if self.init_maze_step < self.num_cars: + return state, 0.0, False, False, {} + else: + # 进入阶段 2：走迷宫 + self.phase = 2 + return state, 0.0, False, False, {} elif self.phase == 2: # 阶段 2：路径规划（走迷宫） current_car = self.current_car_index + current_row, current_col = self.car_pos[current_car] # 当前动作 a 为 1 维连续动作，映射到四个方向 if a < 0.2: @@ -223,18 +206,16 @@ class PartitionMazeEnv(gym.Env): else: move_dir = 'stay' - current_row, current_col = self.car_pos[current_car] - # 初始化新的行、列为当前值 new_row, new_col = current_row, current_col - if move_dir == 'up' and current_row < len(h_boundaries) - 1: + if move_dir == 'up' and current_row < len(self.h_boundaries) - 2: new_row = current_row + 1 elif move_dir == 'down' and current_row > 0: new_row = current_row - 1 elif move_dir == 'left' and current_col > 0: new_col = current_col - 1 - elif move_dir == 'right' and current_col < len(v_boundaries) - 1: + elif move_dir == 'right' and current_col < len(self.v_boundaries) - 2: new_col = current_col + 1 # 如果移动不合法，或者动作为stay，则保持原位置 # TODO 移动不合法，加一些惩罚 @@ -242,47 +223,49 @@ class PartitionMazeEnv(gym.Env): # 更新车辆位置 self.car_pos[current_car] = [new_row, new_col] if new_row != current_row or new_col != current_col: - self.car_traj[current_car].append(np.array(new_row, new_col)) + self.car_traj[current_car].append([new_row, new_col]) self.step_count += 1 self.current_car_index = ( self.current_car_index + 1) % self.num_cars # 更新访问标记：将新网格标记为已访问 - self.rectangles[(new_col, new_col)]['is_visited'] = True + self.rectangles[(new_row, new_col)]['is_visited'] = True # 观察状态 state = np.concatenate( [self.partition_values, np.array(self.car_pos).flatten()]) + reward = 0 # Episode 终止条件：所有网格均被访问或步数达到上限 - done = all([rec['is_visited'] for rec in self.rectangles]) or ( + done = all([value['is_visited'] for _, value in self.rectangles.items()]) or ( self.step_count >= self.MAX_STEPS) - if done and np.all(self.visited_grid == 1): + if done and all([value['is_visited'] for _, value in self.rectangles.items()]): # 区域覆盖完毕，根据轨迹计算各车队的执行时间 T = max([self._compute_motorcade_time(idx) for idx in range(self.num_cars)]) - reward += 10.0 # TODO 奖励与greedy比较 + reward += -(T - self.BASE_LINE) elif done and self.step_count >= self.MAX_STEPS: - reward -= 100 + reward += -100 return state, reward, done, False, {} def _compute_motorcade_time(self, idx): - flight_time = sum(self.rectangles[point]['flight_time'] + flight_time = sum(self.rectangles[tuple(point)]['flight_time'] for point in self.car_traj[idx]) - bs_time = sum(self.rectangles[point]['comp_bs_time'] + bs_time = sum(self.rectangles[tuple(point)]['bs_time'] for point in self.car_traj[idx]) # 计算车的移动时间，首先在轨迹的首尾添加上大区域中心 + car_time = 0 self.car_traj[idx].append([0.5, 0.5]) self.car_traj[idx].insert(0, [0.5, 0.5]) - for i in range(len(self.car_traj[idx])): + for i in range(len(self.car_traj[idx]) - 1): 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 + car_time += math.dist(first_point, second_point) * \ + self.H * self.W * self.car_time_factor - return max(car_time + flight_time, bs_time) + return max(float(car_time) + flight_time, bs_time) def render(self): if self.phase == 0: @@ -291,5 +274,4 @@ class PartitionMazeEnv(gym.Env): 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}")