381 lines
13 KiB
Python
381 lines
13 KiB
Python
"""
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3x3视野贪心资源收集算法
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该模块实现了一个基于3x3视野的贪心算法，用于在迷宫中收集资源。
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算法特点：
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1. 每次移动时只考虑当前位置周围3x3范围内的资源
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2. 优先选择正价值资源（金币），如果没有则选择损失最小的负价值资源（陷阱）
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3. 只能进行上下左右四个方向的移动
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4. 当视野内没有资源时，会进行探索性移动
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5. 避免无限循环，当连续多步无法找到新资源时会自动停止
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使用方法：
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```python
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from greedy_3x3_algorithm import Greedy3x3Algorithm
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# 创建迷宫（二维数组，[y][x]格式）
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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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# 创建算法实例并运行
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algorithm = Greedy3x3Algorithm(maze)
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result = algorithm.run()
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# 获取结果
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print(f"总价值: {result['total_value']}")
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print(f"路径: {result['path']}")
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```
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"""
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import copy
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from collections import deque
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class Greedy3x3Algorithm:
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"""
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3x3视野贪心资源收集算法
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该算法在每个位置只考虑周围3x3范围内的资源，选择价值最高的资源进行收集。
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如果视野内没有资源，则进行探索性移动。
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"""
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def __init__(self, maze, start_pos=None, end_pos=None, debug=False):
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"""
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初始化算法
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Args:
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maze: 二维列表，表示迷宫地图 ([y][x]格式)
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start_pos: 起始位置 (x, y)，默认自动寻找's'
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end_pos: 目标位置 (x, y)，默认自动寻找'e'
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debug: 是否输出调试信息
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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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self.debug = debug
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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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# 初始化状态
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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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self.explored_positions = set([self.start_pos])
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if self.debug:
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print(f"3x3视野贪心算法初始化")
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print(f"迷宫大小: {self.rows}x{self.cols}")
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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，返回(x, y)格式"""
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for y in range(self.rows):
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for x in range(self.cols):
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if self.maze[y][x].lower() == target.lower():
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return (x, y)
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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 (x, y)
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Returns:
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dict: {(x, y): cell_value} 形式的字典
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"""
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x, y = pos
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vision = {}
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# 遍历3x3范围
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for dx in range(-1, 2):
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for dy in range(-1, 2):
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new_x, new_y = x + dx, y + dy
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# 检查边界
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if 0 <= new_x < self.cols and 0 <= new_y < self.rows:
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vision[(new_x, new_y)] = self.maze[new_y][new_x]
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return vision
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def get_adjacent_positions(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 (x, y)
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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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x, y = pos
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adjacent = []
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# 上下左右四个方向
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directions = [(0, -1), (0, 1), (-1, 0), (1, 0)] # 上、下、左、右
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for dx, dy in directions:
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new_x, new_y = x + dx, y + dy
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# 检查边界和可移动性
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if (0 <= new_x < self.cols and
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0 <= new_y < self.rows and
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self._can_move_to((new_x, new_y))):
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adjacent.append((new_x, new_y))
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return adjacent
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def _can_move_to(self, pos):
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"""检查是否可以移动到指定位置"""
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x, y = pos
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cell = self.maze[y][x]
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return cell != '1' # 不能移动到墙壁
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def _evaluate_resource_value(self, cell):
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"""评估资源的价值"""
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if cell.startswith('g'):
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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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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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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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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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adjacent_positions = self.get_adjacent_positions(self.current_pos)
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best_pos = None
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best_value = float('-inf')
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# 只考虑相邻且在视野内的位置
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for pos in adjacent_positions:
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if pos in vision and pos not in self.visited_resources:
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cell = vision[pos]
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value = self._evaluate_resource_value(cell)
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if value != 0 and value > best_value:
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best_value = value
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best_pos = pos
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return best_pos, best_value if best_pos else 0
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def _find_exploration_target(self):
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"""当视野内没有资源时，寻找探索目标"""
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adjacent = self.get_adjacent_positions(self.current_pos)
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# 优先选择未探索的位置
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unexplored = [pos for pos in adjacent if pos not in self.explored_positions]
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if unexplored:
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return unexplored[0]
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# 如果所有相邻位置都探索过，选择任意一个
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if adjacent:
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return adjacent[0]
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return None
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def _collect_resource(self, pos):
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"""收集指定位置的资源"""
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x, y = pos
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cell = self.maze[y][x]
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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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if self.debug:
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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(self, max_moves=1000, max_stuck=20):
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"""
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运行3x3视野贪心资源收集算法
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Args:
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max_moves: 最大移动步数，防止无限循环
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max_stuck: 连续无资源的最大步数
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Returns:
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dict: 包含路径、收集的资源等信息的结果字典
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"""
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if self.debug:
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print("\\n开始3x3视野贪心资源收集...")
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moves = 0
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stuck_count = 0
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while moves < max_moves and stuck_count < max_stuck:
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moves += 1
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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 not None:
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if self.debug:
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print(f"第{moves}步: 发现视野内资源 位置{best_resource_pos}, 价值{best_value}")
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# 移动到资源位置并收集
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self.current_pos = best_resource_pos
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self.path.append(best_resource_pos)
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self.explored_positions.add(best_resource_pos)
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self._collect_resource(best_resource_pos)
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stuck_count = 0 # 重置无资源计数
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else:
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# 视野内没有资源，进行探索性移动
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exploration_target = self._find_exploration_target()
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if exploration_target:
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if self.debug:
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print(f"第{moves}步: 视野内无资源，探索移动到 {exploration_target}")
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self.current_pos = exploration_target
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self.path.append(exploration_target)
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self.explored_positions.add(exploration_target)
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stuck_count += 1
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else:
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if self.debug:
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print(f"第{moves}步: 无法进行任何移动，结束收集")
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break
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if self.debug:
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if moves >= max_moves:
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print(f"达到最大移动步数 {max_moves}，结束收集")
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elif stuck_count >= max_stuck:
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print(f"连续 {max_stuck} 步未找到资源，结束收集")
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print("3x3视野资源收集完成！")
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return self._get_result()
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def _get_result(self):
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"""获取算法执行结果"""
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return {
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'path': self.path.copy(),
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'path_yx_format': [(y, x) for (x, y) in self.path], # 兼容现有代码的(y,x)格式
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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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'explored_positions_count': len(self.explored_positions),
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'algorithm_name': '3x3视野贪心算法'
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}
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def get_marked_maze(self):
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"""
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获取标记了路径的迷宫
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Returns:
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list: 标记后的迷宫，S=起点, E=终点, *=已收集资源, .=路径
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"""
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marked_maze = copy.deepcopy(self.original_maze)
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# 标记路径点
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for i, (x, y) in enumerate(self.path):
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if (x, y) == self.start_pos:
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marked_maze[y][x] = 'S' # 起点
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elif (x, y) == self.end_pos:
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marked_maze[y][x] = 'E' # 终点
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elif (x, y) in [r['position'] for r in self.collected_resources]:
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marked_maze[y][x] = '*' # 已收集资源
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else:
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marked_maze[y][x] = '.' # 路径点
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return marked_maze
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def print_result(self):
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"""打印算法执行结果"""
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result = self._get_result()
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print("\\n=== 3x3视野贪心算法执行结果 ===")
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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"探索位置数: {result['explored_positions_count']}")
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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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if result['collected_resources']:
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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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else:
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print("\\n未收集到任何资源")
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# 显示标记后的迷宫
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marked_maze = self.get_marked_maze()
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print("\\n标记路径后的迷宫:")
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print("S: 起点, E: 终点, *: 已收集资源, .: 路径")
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for row in marked_maze:
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print(' '.join(f"{cell:>2}" for cell in row))
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def demo():
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"""演示函数"""
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# 创建示例迷宫
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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("=== 3x3视野贪心算法演示 ===")
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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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# 运行算法
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algorithm = Greedy3x3Algorithm(demo_maze, debug=True)
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result = algorithm.run()
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# 打印结果
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algorithm.print_result()
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return algorithm, result
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if __name__ == "__main__":
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demo()
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