449 lines
15 KiB
Python
449 lines
15 KiB
Python
import copy
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from collections import deque
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class GreedyResourceCollector:
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"""
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基于贪心算法的资源收集器
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每次移动时选择3x3视野范围内最高价值的资源
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只能进行上下左右移动
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"""
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def __init__(self, maze, start_pos=None, end_pos=None):
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"""
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初始化贪心资源收集器
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Args:
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maze: 迷宫地图，2D列表
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start_pos: 起始位置 (row, col)，如果为None则自动寻找
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end_pos: 目标位置 (row, col)，如果为None则自动寻找
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"""
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self.original_maze = copy.deepcopy(maze)
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self.maze = copy.deepcopy(maze)
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self.rows = len(maze)
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self.cols = len(maze[0]) if self.rows > 0 else 0
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# 寻找起始位置和目标位置
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self.start_pos = start_pos or self._find_position('s')
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self.end_pos = end_pos or self._find_position('e')
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if not self.start_pos:
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raise ValueError("无法找到起始位置 's'")
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if not self.end_pos:
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raise ValueError("无法找到目标位置 'e'")
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self.current_pos = self.start_pos
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self.path = [self.start_pos]
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self.collected_resources = []
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self.total_value = 0
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self.visited_resources = set()
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print(f"起始位置: {self.start_pos}")
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print(f"目标位置: {self.end_pos}")
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def _find_position(self, target):
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"""寻找地图中指定字符的位置"""
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for i in range(self.rows):
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for j in range(self.cols):
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if self.maze[i][j].lower() == target.lower():
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return (i, j)
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return None
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def get_3x3_vision(self, pos):
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"""
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获取以pos为中心的3x3视野范围内的所有单元格
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Args:
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pos: 当前位置 (row, col)
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Returns:
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dict: {(row, col): cell_value} 形式的字典
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"""
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row, col = pos
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vision = {}
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# 遍历3x3范围
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for dr in range(-1, 2):
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for dc in range(-1, 2):
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new_row, new_col = row + dr, col + dc
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# 检查边界
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if 0 <= new_row < self.rows and 0 <= new_col < self.cols:
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vision[(new_row, new_col)] = self.maze[new_row][new_col]
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return vision
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def get_adjacent_cells(self, pos):
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"""
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获取当前位置的上下左右四个相邻位置
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Args:
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pos: 当前位置 (row, col)
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Returns:
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list: 可移动的相邻位置列表
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"""
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row, col = pos
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adjacent = []
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# 上下左右四个方向
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directions = [(-1, 0), (1, 0), (0, -1), (0, 1)]
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for dr, dc in directions:
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new_row, new_col = row + dr, col + dc
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# 检查边界和可移动性
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if (0 <= new_row < self.rows and
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0 <= new_col < self.cols and
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self.can_move_to((new_row, new_col))):
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adjacent.append((new_row, new_col))
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return adjacent
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def can_move_to(self, pos):
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"""
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检查是否可以移动到指定位置
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Args:
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pos: 目标位置 (row, col)
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Returns:
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bool: 是否可以移动
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"""
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row, col = pos
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cell = self.maze[row][col]
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# 不能移动到墙壁
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if cell == '1':
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return False
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return True
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def evaluate_resource_value(self, cell):
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"""
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评估资源的价值
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Args:
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cell: 单元格内容
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Returns:
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int: 资源价值，正数表示收益，负数表示损失
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"""
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if cell.startswith('g'):
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# 金币资源，提取数值
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try:
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return int(cell[1:])
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except ValueError:
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return 0
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elif cell.startswith('t'):
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# 陷阱资源，提取数值并取负
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try:
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return -int(cell[1:])
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except ValueError:
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return 0
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else:
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# 其他类型单元格没有资源价值
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return 0
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def find_best_resource_in_vision(self):
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"""
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在3x3视野范围内找到价值最高的可到达资源
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如果视野内没有正价值资源，则考虑移动到更有利的位置
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Returns:
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tuple: (最佳资源位置, 资源价值) 或 (None, 0)
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"""
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vision = self.get_3x3_vision(self.current_pos)
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best_pos = None
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best_value = float('-inf')
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# 首先只考虑正价值资源
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for pos, cell in vision.items():
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# 跳过已访问的资源
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if pos in self.visited_resources:
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continue
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# 跳过当前位置
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if pos == self.current_pos:
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continue
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# 跳过不可移动的位置
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if not self.can_move_to(pos):
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continue
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# 检查是否为资源
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value = self.evaluate_resource_value(cell)
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if value > 0 and value > best_value: # 只考虑正价值资源
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# 确保可以到达这个位置
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path_to_resource = self.find_path_to_target(pos)
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if path_to_resource: # 可以到达
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best_value = value
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best_pos = pos
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# 如果视野内没有正价值资源，考虑负价值资源（陷阱）
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if best_pos is None:
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for pos, cell in vision.items():
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if pos in self.visited_resources or pos == self.current_pos:
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continue
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if not self.can_move_to(pos):
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continue
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value = self.evaluate_resource_value(cell)
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if value < 0 and value > best_value: # 选择损失最小的陷阱
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path_to_resource = self.find_path_to_target(pos)
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if path_to_resource:
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best_value = value
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best_pos = pos
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# 如果视野内完全没有资源，寻找最近的正价值资源
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if best_pos is None:
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best_pos, best_value = self.find_nearest_valuable_resource()
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return best_pos, best_value if best_pos else 0
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def find_nearest_valuable_resource(self):
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"""
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在整个地图上寻找最近的高价值正资源
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Returns:
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tuple: (最佳资源位置, 资源价值) 或 (None, 0)
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"""
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best_pos = None
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best_score = float('-inf')
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for i in range(self.rows):
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for j in range(self.cols):
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pos = (i, j)
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cell = self.maze[i][j]
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# 跳过已访问的资源
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if pos in self.visited_resources:
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continue
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# 跳过当前位置
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if pos == self.current_pos:
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continue
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# 检查是否为正价值资源
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value = self.evaluate_resource_value(cell)
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if value > 0: # 只考虑正价值资源
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# 计算到达该资源的路径
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path_to_resource = self.find_path_to_target(pos)
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if path_to_resource:
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# 计算性价比：价值/距离
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distance = len(path_to_resource)
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score = value / distance
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if score > best_score:
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best_score = score
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best_pos = pos
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if best_pos:
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value = self.evaluate_resource_value(self.maze[best_pos[0]][best_pos[1]])
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return best_pos, value
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return None, 0
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def find_path_to_target(self, target_pos):
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"""
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使用BFS找到到目标位置的最短路径
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Args:
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target_pos: 目标位置 (row, col)
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Returns:
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list: 从当前位置到目标位置的路径（不包含当前位置）
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"""
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if self.current_pos == target_pos:
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return []
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queue = deque([(self.current_pos, [])])
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visited = {self.current_pos}
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while queue:
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pos, path = queue.popleft()
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# 获取相邻位置
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for next_pos in self.get_adjacent_cells(pos):
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if next_pos in visited:
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continue
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new_path = path + [next_pos]
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# 找到目标
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if next_pos == target_pos:
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return new_path
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visited.add(next_pos)
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queue.append((next_pos, new_path))
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return [] # 无法到达
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def collect_resource(self, pos):
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"""
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收集指定位置的资源
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Args:
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pos: 资源位置 (row, col)
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"""
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row, col = pos
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cell = self.maze[row][col]
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value = self.evaluate_resource_value(cell)
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if value != 0:
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self.collected_resources.append({
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'position': pos,
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'type': cell,
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'value': value
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})
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self.total_value += value
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self.visited_resources.add(pos)
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print(f"收集资源: 位置{pos}, 类型{cell}, 价值{value}, 总价值{self.total_value}")
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def run_greedy_collection(self):
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"""
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运行贪心资源收集算法
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Returns:
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dict: 包含路径、收集的资源等信息
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"""
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print("开始贪心资源收集...")
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while True:
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# 在3x3视野内寻找最佳资源
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best_resource_pos, best_value = self.find_best_resource_in_vision()
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if best_resource_pos is None:
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print("视野内没有更多资源可收集")
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break
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print(f"发现最佳资源: 位置{best_resource_pos}, 价值{best_value}")
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# 计算到最佳资源的路径
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path_to_resource = self.find_path_to_target(best_resource_pos)
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if not path_to_resource:
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print(f"无法到达资源位置{best_resource_pos}")
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self.visited_resources.add(best_resource_pos) # 标记为无法到达
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continue
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# 移动到资源位置
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for next_pos in path_to_resource:
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self.current_pos = next_pos
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self.path.append(next_pos)
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print(f"移动到: {next_pos}")
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# 收集资源
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self.collect_resource(best_resource_pos)
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print("资源收集完成！")
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return self.get_collection_result()
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def get_collection_result(self):
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"""
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获取收集结果
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Returns:
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dict: 包含路径、资源、统计信息的字典
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"""
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return {
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'path': self.path.copy(),
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'collected_resources': self.collected_resources.copy(),
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'total_value': self.total_value,
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'total_moves': len(self.path) - 1,
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'resources_count': len(self.collected_resources),
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'start_pos': self.start_pos,
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'end_pos': self.end_pos,
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'final_pos': self.current_pos
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}
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def print_result_summary(self):
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"""打印收集结果摘要"""
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result = self.get_collection_result()
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print("\n=== 贪心资源收集结果摘要 ===")
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print(f"起始位置: {result['start_pos']}")
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print(f"最终位置: {result['final_pos']}")
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print(f"总移动步数: {result['total_moves']}")
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print(f"收集资源数量: {result['resources_count']}")
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print(f"资源总价值: {result['total_value']}")
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print("\n收集的资源详情:")
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for i, resource in enumerate(result['collected_resources'], 1):
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print(f" {i}. 位置{resource['position']}: {resource['type']} (价值: {resource['value']})")
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print(f"\n完整移动路径: {' -> '.join(map(str, result['path']))}")
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def visualize_path_on_maze(self):
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"""
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在迷宫上可视化移动路径
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Returns:
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list: 标记了路径的迷宫副本
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"""
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visual_maze = copy.deepcopy(self.original_maze)
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# 标记路径
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for i, pos in enumerate(self.path):
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row, col = pos
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if pos == self.start_pos:
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visual_maze[row][col] = 'S' # 起点
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elif pos == self.end_pos:
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visual_maze[row][col] = 'E' # 终点
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elif pos in [r['position'] for r in self.collected_resources]:
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# 已收集的资源位置
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visual_maze[row][col] = '*'
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else:
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# 路径点
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visual_maze[row][col] = str(i % 10)
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return visual_maze
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def print_visual_maze(self):
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"""打印可视化的迷宫"""
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visual_maze = self.visualize_path_on_maze()
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print("\n=== 路径可视化迷宫 ===")
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print("S: 起点, E: 终点, *: 已收集资源, 数字: 路径步骤")
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for row in visual_maze:
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print(' '.join(f"{cell:>2}" for cell in row))
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def demo_greedy_resource_collection():
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"""演示贪心资源收集算法"""
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# 创建一个示例迷宫
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demo_maze = [
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['s', '0', 'g5', '1', 't3'],
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['0', '1', '0', '0', 'g2'],
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['g3', '0', '1', 't2', '0'],
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['0', 't1', '0', '0', 'g4'],
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['1', '0', 'g1', '0', 'e']
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]
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print("=== 贪心资源收集算法演示 ===")
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print("迷宫说明:")
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print(" s: 起点, e: 终点")
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print(" g数字: 金币资源 (正收益)")
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print(" t数字: 陷阱资源 (负收益)")
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print(" 0: 可通行路径, 1: 墙壁")
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print("\n原始迷宫:")
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for row in demo_maze:
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print(' '.join(f"{cell:>2}" for cell in row))
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# 创建贪心收集器并运行
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collector = GreedyResourceCollector(demo_maze)
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result = collector.run_greedy_collection()
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# 打印结果
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collector.print_result_summary()
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collector.print_visual_maze()
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return collector, result
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if __name__ == "__main__":
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# 运行演示
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collector, result = demo_greedy_resource_collection()
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