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修改环境

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weixin_46229132 2025-03-19 14:22:24 +08:00
parent e35dd10326
commit 7ca5ce08b1
3 changed files with 48 additions and 17 deletions
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3
DDPG_solver/utils.py
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@ -50,7 +50,8 @@ def evaluate_policy(env, agent, turns = 3):
action_series.append(a[0]) action_series.append(a[0])
total_scores += r total_scores += r
s = s_next s = s_next
print(np.round(action_series, 3)) print('action series: ', np.round(action_series, 3))
print('state: {s_next}')
return int(total_scores/turns) return int(total_scores/turns)

60
env.py
View File

@ -53,11 +53,11 @@ class PartitionMazeEnv(gym.Env):
low=0.0, high=1.0, shape=(1,), dtype=np.float32) low=0.0, high=1.0, shape=(1,), dtype=np.float32)
# 定义观察空间为8维向量 # 定义观察空间为8维向量
# TODO 返回的状态目前只有位置坐标
# 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 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) # 阶段 1 状态：区域访问状态向量（长度为(CUT_NUM/2+1)^2）
max_regions = (self.CUT_NUM // 2 + 1) ** 2
self.observation_space = spaces.Box( self.observation_space = spaces.Box(
low=0.0, high=1.0, shape=(self.CUT_NUM + 2 * self.num_cars,), dtype=np.float32) low=0.0, high=1.0, shape=(self.CUT_NUM + max_regions,), dtype=np.float32)
# 切分阶段相关变量 # 切分阶段相关变量
self.col_cuts = [] # 存储竖切位置（c₁, c₂），当值为0时表示不切 self.col_cuts = [] # 存储竖切位置（c₁, c₂），当值为0时表示不切
@ -86,9 +86,13 @@ class PartitionMazeEnv(gym.Env):
self.car_pos = [(self.H / 2, self.W / 2) for _ in range(self.num_cars)] self.car_pos = [(self.H / 2, self.W / 2) for _ in range(self.num_cars)]
self.car_traj = [[] for _ in range(self.num_cars)] self.car_traj = [[] for _ in range(self.num_cars)]
self.current_car_index = 0 self.current_car_index = 0
# 状态：前 4 维为 partition_values，其余补 0
state = np.concatenate( # 状态：前 4 维为 partition_values，其余为区域访问状态（初始全0）
[self.partition_values, np.zeros(np.array(self.car_pos).flatten().shape[0], dtype=np.float32)]) max_regions = (self.CUT_NUM // 2 + 1) ** 2
state = np.concatenate([
self.partition_values,
np.zeros(max_regions, dtype=np.float32)
])
return state return state
def step(self, action): def step(self, action):
@ -103,8 +107,10 @@ class PartitionMazeEnv(gym.Env):
self.partition_step += 1 self.partition_step += 1
# 构造当前状态：前 partition_step 个为已决策值，其余为 0，再补 7 个 0 # 构造当前状态：前 partition_step 个为已决策值，其余为 0，再补 7 个 0
state = np.concatenate( state = np.concatenate([
[self.partition_values, np.zeros(np.array(self.car_pos).flatten().shape[0], dtype=np.float32)]) self.partition_values,
np.zeros((self.CUT_NUM // 2 + 1) ** 2, dtype=np.float32)
])
# 如果未完成 4 步，则仍处于切分阶段，不发奖励，done 为 False # 如果未完成 4 步，则仍处于切分阶段，不发奖励，done 为 False
if self.partition_step < self.CUT_NUM: if self.partition_step < self.CUT_NUM:
@ -153,8 +159,12 @@ class PartitionMazeEnv(gym.Env):
if not valid_partition: if not valid_partition:
reward = -10000 reward = -10000
state = np.concatenate( # 状态：前 4 维为 partition_values，其余为区域访问状态（初始全0）
[self.partition_values, np.zeros(np.array(self.car_pos).flatten().shape[0], dtype=np.float32)]) max_regions = (self.CUT_NUM // 2 + 1) ** 2
state = np.concatenate([
self.partition_values,
np.zeros(max_regions, dtype=np.float32)
])
return state, reward, True, False, {} return state, reward, True, False, {}
else: else:
# 进入阶段 1：初始化迷宫 # 进入阶段 1：初始化迷宫
@ -183,9 +193,19 @@ class PartitionMazeEnv(gym.Env):
# 进入阶段 2：走迷宫 # 进入阶段 2：走迷宫
self.phase = 2 self.phase = 2
state = np.concatenate(
[self.partition_values, np.array(self.car_pos).flatten()] # 构造访问状态向量
) max_regions = (self.CUT_NUM // 2 + 1) ** 2
visit_status = np.zeros(max_regions, dtype=np.float32)
# 将实际区域的访问状态填入向量
for i in range(len(self.row_cuts) - 1):
for j in range(len(self.col_cuts) - 1):
idx = i * (len(self.col_cuts) - 1) + j
visit_status[idx] = float(self.rectangles[(i, j)]['is_visited'])
for i in range(idx + 1, max_regions):
visit_status[i] = 100
state = np.concatenate([self.partition_values, visit_status])
return state, reward, False, False, {} return state, reward, False, False, {}
elif self.phase == 2: elif self.phase == 2:
@ -250,8 +270,18 @@ class PartitionMazeEnv(gym.Env):
self.rectangles[(new_row, new_col)]['is_visited'] = True self.rectangles[(new_row, new_col)]['is_visited'] = True
# 观察状态 # 观察状态
state = np.concatenate( # 构造访问状态向量
[self.partition_values, np.array(self.car_pos).flatten()]) max_regions = (self.CUT_NUM // 2 + 1) ** 2
visit_status = np.zeros(max_regions, dtype=np.float32)
# 将实际区域的访问状态填入向量
for i in range(len(self.row_cuts) - 1):
for j in range(len(self.col_cuts) - 1):
idx = i * (len(self.col_cuts) - 1) + j
visit_status[idx] = float(self.rectangles[(i, j)]['is_visited'])
for i in range(idx + 1, max_regions):
visit_status[i] = 100
state = np.concatenate([self.partition_values, visit_status])
# Episode 终止条件：所有网格均被访问或步数达到上限 # Episode 终止条件：所有网格均被访问或步数达到上限
done = all([value['is_visited'] for _, value in self.rectangles.items()]) or ( done = all([value['is_visited'] for _, value in self.rectangles.items()]) or (

2
human_action.py
View File

@ -6,7 +6,7 @@ env = PartitionMazeEnv()
state = env.reset() state = env.reset()
print(state) print(state)
action_series = [[0.1], [0.2], [0.4], [0], [0.1]] action_series = [[0], [0], [0.4], [0], [0.1]]
# action_series = [0, 0, 3, 0, 0, 10] # action_series = [0, 0, 3, 0, 0, 10]
for i in range(100): for i in range(100):