构造真空吸尘器世界的搜索Agent并评价其性能,环境是3×3方格,每个方格里有灰尘的概率是0.2。在性能度量中不仅包括路径代价也要包括搜索开销,使用合理的转换比率。Python来解决
时间: 2024-04-14 08:28:00 浏览: 25
下面是根据你的要求构建真空吸尘器世界的搜索 Agent,并评价其性能的 Python 代码:
```python
import random
from collections import deque
# 定义状态类
class State:
def __init__(self, location, direction, grid):
self.location = location # 吸尘器位置坐标
self.direction = direction # 吸尘器方向
self.grid = grid # 环境状态矩阵
# 定义操作符
actions = ['UP', 'DOWN', 'LEFT', 'RIGHT']
movements = {
'UP': (-1, 0),
'DOWN': (1, 0),
'LEFT': (0, -1),
'RIGHT': (0, 1)
}
def move(state, action):
# 根据操作进行移动
row, col = state.location
dr, dc = movements[action]
new_location = (row + dr, col + dc)
new_direction = action
new_grid = state.grid.copy()
if 0 <= row + dr < len(new_grid) and 0 <= col + dc < len(new_grid[0]):
new_grid[row][col] = 0
return State(new_location, new_direction, new_grid)
# 实现广度优先搜索算法
def bfs(initial_state):
queue = deque([initial_state])
while queue:
state = queue.popleft()
if is_goal_state(state):
return state
for action in actions:
new_state = move(state, action)
queue.append(new_state)
return None
# 判断是否为目标状态
def is_goal_state(state):
for row in state.grid:
for cell in row:
if cell != 0:
return False
return True
# 构造初始状态
def generate_initial_state():
location = (random.randint(0, 2), random.randint(0, 2))
direction = random.choice(actions)
grid = [[1 if random.random() < 0.2 else 0 for _ in range(3)] for _ in range(3)]
return State(location, direction, grid)
# 运行搜索算法
initial_state = generate_initial_state()
goal_state = bfs(initial_state)
# 评价性能
def calculate_path_cost(state):
path_cost = 0
for row in state.grid:
for cell in row:
if cell != 0:
path_cost += 1
return path_cost
def calculate_search_cost():
search_cost = 0
queue = deque([initial_state])
while queue:
state = queue.popleft()
search_cost += 1
if is_goal_state(state):
return search_cost
for action in actions:
new_state = move(state, action)
queue.append(new_state)
return search_cost
path_cost = calculate_path_cost(goal_state)
search_cost = calculate_search_cost()
print("目标状态路径代价:", path_cost)
print("搜索开销:", search_cost)
```
这段代码中,我们首先定义了一个状态类 `State`,其中包含了吸尘器的位置、方向和环境状态矩阵。然后,我们定义了操作符和移动函数,实现了广度优先搜索算法来搜索最优路径。接着,我们构造了初始状态,并运行搜索算法得到目标状态。最后,我们评价了性能,包括路径代价和搜索开销。
请注意,在生成初始状态的函数 `generate_initial_state()` 中,我们使用了 `random.randint(0, 2)` 来生成吸尘器的初始位置坐标。而在评价性能的函数 `calculate_path_cost(state)` 和 `calculate_search_cost()` 中,我们分别计算了路径代价和搜索开销。
希望这段代码能满足你的需求。如果有任何问题,请随时提问。
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