修改env数据结构
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weixin_46229132
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aecd86b245
70
PPO/env.py
70
PPO/env.py
@ -54,8 +54,8 @@ class PartitionMazeEnv(gym.Env):
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low=0.0, high=1.0, shape=(4 + 2 * self.num_cars,), 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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self.col_cuts = [] # 存储竖切位置（c₁, c₂），当值为0时表示不切
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self.row_cuts = [] # 存储横切位置（r₁, r₂）
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self.init_maze_step = 0
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@ -64,7 +64,7 @@ class PartitionMazeEnv(gym.Env):
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self.BASE_LINE = 3500.0 # 基准时间，通过greedy或者蒙特卡洛计算出来
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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_pos = [(0, 0) 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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@ -73,13 +73,13 @@ class PartitionMazeEnv(gym.Env):
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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.col_cuts = []
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self.row_cuts = []
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self.init_maze_step = 0
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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_pos = [(0, 0) 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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@ -112,19 +112,19 @@ class PartitionMazeEnv(gym.Env):
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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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vertical_cuts = vert if vert else []
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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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self.col_cuts = [0.0] + vertical_cuts + [1.0]
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self.row_cuts = [0.0] + 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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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] + self.row_cuts[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_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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@ -139,7 +139,7 @@ class PartitionMazeEnv(gym.Env):
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bs_time = self.bs_time_factor * (1 - rho) * d
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self.rectangles[(i, j)] = {
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'center': ((v_boundaries[j+1] + v_boundaries[j]) * self.W / 2, (h_boundaries[i] + h_boundaries[i+1]) * self.H / 2),
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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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'is_visited': False
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@ -148,7 +148,7 @@ class PartitionMazeEnv(gym.Env):
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break
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if not valid_partition:
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reward = -100
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reward = -10000
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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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@ -157,9 +157,9 @@ class PartitionMazeEnv(gym.Env):
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# 进入阶段 1：初始化迷宫
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self.phase = 1
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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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# 所有车队从整个区域的中心出发
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self.car_pos = [(len(self.row_cuts) - 2 / 2, len(self.col_cuts) -2 / 2)
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for _ in range(self.num_cars)]
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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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@ -167,17 +167,17 @@ class PartitionMazeEnv(gym.Env):
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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.v_boundaries) - 1) * \
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(len(self.h_boundaries) - 1)
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num_regions = (len(self.col_cuts) - 1) * \
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(len(self.row_cuts) - 1)
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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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# 将index映射到笛卡尔坐标
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coord = [target_region_index // (len(self.v_boundaries) - 1),
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target_region_index % (len(self.v_boundaries) - 1)]
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coord = (target_region_index // (len(self.col_cuts) - 1),
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target_region_index % (len(self.col_cuts) - 1))
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self.car_pos[self.init_maze_step] = coord
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self.car_traj[self.init_maze_step].append(coord)
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self.rectangles[tuple(coord)]['is_visited'] = True
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self.rectangles[coord]['is_visited'] = True
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# 计数
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self.init_maze_step += 1
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@ -210,21 +210,21 @@ class PartitionMazeEnv(gym.Env):
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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(self.h_boundaries) - 2:
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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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if move_dir == 'up' and current_row > 0:
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new_row = current_row - 1
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elif move_dir == 'down' and current_row < len(self.row_cuts) - 2:
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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(self.v_boundaries) - 2:
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elif move_dir == 'right' and current_col < len(self.col_cuts) - 2:
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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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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([new_row, new_col])
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self.car_traj[current_car].append((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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@ -260,15 +260,15 @@ class PartitionMazeEnv(gym.Env):
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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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car_time = 0
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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])
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for i in range(len(self.car_traj[idx]) - 1):
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first_point = self.car_traj[idx][i]
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second_point = self.car_traj[idx][i + 1]
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car_time += math.dist(self.rectangles[tuple(first_point)]['center'], self.rectangles[tuple(second_point)]['center']) * \
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car_time += math.dist(self.rectangles[first_point]['center'], self.rectangles[second_point]['center']) * \
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self.car_time_factor
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car_time += math.dist(self.rectangles[tuple(self.car_traj[idx][0])]['center'], [self.W / 2, self.H / 2]) * self.car_time_factor
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car_time += math.dist(self.rectangles[tuple(self.car_traj[idx][-1])]['center'], [self.W / 2, self.H / 2]) * self.car_time_factor
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car_time += math.dist(self.rectangles[self.car_traj[idx][0]]['center'], [
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self.H / 2, self.W / 2]) * self.car_time_factor
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car_time += math.dist(self.rectangles[self.car_traj[idx][-1]]['center'], [
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self.H / 2, self.W / 2]) * self.car_time_factor
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return max(float(car_time) + flight_time, bs_time)
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