618 lines
20 KiB
Python
618 lines
20 KiB
Python
import math
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from maze import *
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import math
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import copy
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from collections import deque
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class GreedyPlayer:
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def __init__(self, map_data, start=None, end=None):
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"""初始化GreedyPlayer对象"""
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self.map_data = map_data
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self.rows = len(map_data)
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self.cols = len(map_data[0]) if self.rows > 0 else 0
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self.start = start
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self.end = end
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self.path = []
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self.total_reward = 0
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self.visited = set()
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self.marked_map = []
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# 如果未指定起点和终点，自动查找
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if not self.start or not self.end:
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self._find_start_end()
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def _find_start_end(self):
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"""自动查找地图中的起点(s)和终点(e)"""
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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.map_data[y][x] == 's' or self.map_data[y][x] == 'S':
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self.start = (x, y)
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elif self.map_data[y][x] == 'e' or self.map_data[y][x] == 'E':
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self.end = (x, y)
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print(f"起点: {self.start}, 终点: {self.end}")
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def get_visible_cells(self, x, y, visibility=1):
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"""获取以(x,y)为中心的上下左右四个方向的单元格信息"""
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visible = {}
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# 只考虑上下左右四个方向（dx或dy为±1，另一个为0）
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directions = [(-1, 0), (1, 0), (0, -1), (0, 1)]
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for dx, dy in directions:
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nx, ny = x + dx, y + dy
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if 0 <= nx < self.cols and 0 <= ny < self.rows:
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cell = self.map_data[ny][nx]
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distance = 1 # 上下左右移动距离为1
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visible[(nx, ny)] = (cell, distance)
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return visible
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def evaluate_cell(self, cell, distance):
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"""评估单元格的价值，返回奖励/路径的比值"""
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if cell == 's' or cell == 'e':
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return 0 # 起点和终点不参与资源评估
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if cell.startswith('t'):
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try:
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value = -int(cell[1:]) # t表示损失，转为负值
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return value / distance
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except ValueError:
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return 0
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elif cell.startswith('g'):
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try:
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value = int(cell[1:]) # g表示收益，转为正值
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return value / distance
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except ValueError:
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return 0
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return 0 # 0、l、b等不产生资源价值
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def find_path(self):
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"""基于贪心策略的路径规划（只能上下左右移动）"""
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if not self.start or not self.end:
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raise ValueError("地图中未找到起点或终点")
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current = self.start
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self.path = [current]
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self.visited = {current}
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self.total_reward = 0
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while current != self.end:
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x, y = current
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visible = self.get_visible_cells(x, y)
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best_cell = None
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best_value = -float('inf')
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for (nx, ny), (cell, distance) in visible.items():
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# 跳过已访问的位置
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if (nx, ny) in self.visited:
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continue
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# 只允许在0、t、g、l、b上行走
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if cell not in ['0'] and not cell.startswith(('t', 'g', 'l', 'b')):
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continue
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# 评估单元格价值
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value = self.evaluate_cell(cell, distance)
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# 终点具有最高优先级
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if cell == 'e':
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value = float('inf')
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# 选择贪心值最大的单元格
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if value > best_value:
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best_value = value
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best_cell = (nx, ny)
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# 无法找到可行路径
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if best_cell is None:
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print("无法找到通往终点的路径！")
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break
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# 更新当前位置和路径
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current = best_cell
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self.path.append(current)
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self.visited.add(current)
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# 更新总收益（跳过起点和终点）
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if len(self.path) > 1 and len(self.path) < len(self.path) + 1:
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cell = self.map_data[current[1]][current[0]]
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if cell.startswith('t'):
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self.total_reward -= int(cell[1:])
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elif cell.startswith('g'):
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self.total_reward += int(cell[1:])
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self.add_path_to_map()
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return self.path
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def add_path_to_map(self):
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"""在地图上标记路径，上下移动用|，左右移动用-"""
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if not self.path:
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print("没有路径可标记")
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return
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# 创建地图副本，避免修改原始地图
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marked_map = [row.copy() for row in self.map_data]
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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 marked_map[y][x] == 's':
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marked_map[y][x] = 'S' # 标记起点
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elif marked_map[y][x] == 'e':
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marked_map[y][x] = 'E' # 标记终点
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else:
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marked_map[y][x] = '*' # 标记路径点
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# 标记路径线（上下左右）
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for i in range(len(self.path) - 1):
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x1, y1 = self.path[i]
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x2, y2 = self.path[i + 1]
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# 左右移动
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if x1 != x2 and y1 == y2:
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start, end = (x1, x2) if x1 < x2 else (x2, x1)
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for x in range(start, end + 1):
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if marked_map[y1][x] not in ['S', 'E']:
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marked_map[y1][x] = '-'
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# 上下移动
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elif y1 != y2 and x1 == x2:
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start, end = (y1, y2) if y1 < y2 else (y2, y1)
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for y in range(start, end + 1):
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if marked_map[y][x1] not in ['S', 'E']:
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marked_map[y][x1] = '|'
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# 保存标记后的地图
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self.marked_map = marked_map
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return marked_map
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def get_path(self):
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"""返回找到的路径"""
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return self.path
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def get_total_reward(self):
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"""返回总收益"""
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return self.total_reward
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class Greedy3x3ResourceCollector:
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"""
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基于3x3视野的贪心资源收集器
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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, map_data, start=None, end=None):
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"""
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初始化3x3视野贪心资源收集器
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Args:
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map_data: 迷宫地图，2D列表 (注意：这里是[y][x]格式)
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start: 起始位置 (x, y)，如果为None则自动寻找
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end: 目标位置 (x, y)，如果为None则自动寻找
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"""
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self.original_map = copy.deepcopy(map_data)
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self.map_data = copy.deepcopy(map_data)
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self.rows = len(map_data)
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self.cols = len(map_data[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 = start or self._find_position('s')
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self.end = end or self._find_position('e')
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if not self.start:
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raise ValueError("无法找到起始位置 's'")
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if not self.end:
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raise ValueError("无法找到目标位置 'e'")
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self.current_pos = self.start
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self.path = [self.start]
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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])
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print(f"3x3视野贪心算法初始化")
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print(f"起始位置: {self.start}")
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print(f"目标位置: {self.end}")
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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.map_data[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.map_data[new_y][new_x]
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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 (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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"""
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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 (x, y)
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Returns:
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bool: 是否可以移动
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"""
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x, y = pos
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cell = self.map_data[y][x]
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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_3x3_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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best_pos = None
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best_value = float('-inf')
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# 首先尝试找正价值资源
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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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# 检查是否可以直接到达（相邻位置）
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if pos not in self.get_adjacent_cells(self.current_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: # 优先选择正价值资源
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best_value = value
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best_pos = pos
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# 如果没有正价值资源，考虑负价值资源（选择损失最小的）
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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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if pos not in self.get_adjacent_cells(self.current_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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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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"""
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当视野内没有资源时，寻找探索目标
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优先选择未探索过的位置
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"""
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adjacent = self.get_adjacent_cells(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] # 选择第一个未探索的位置
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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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"""
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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 (x, y)
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"""
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x, y = pos
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cell = self.map_data[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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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_3x3_greedy_collection(self, max_moves=1000):
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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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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("\\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 # 连续无法找到资源的次数
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max_stuck = 20 # 最大连续无资源次数
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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_3x3_vision()
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if best_resource_pos is not None:
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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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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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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 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_collection_result()
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def get_collection_result(self):
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"""获取收集结果"""
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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,
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'end_pos': self.end,
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'final_pos': self.current_pos,
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'explored_positions': len(self.explored_positions)
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}
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||
def get_path(self):
|
||
"""返回路径，转换为(y, x)格式以兼容现有代码"""
|
||
# 将(x, y)格式的路径转换为(y, x)格式
|
||
return [(y, x) for (x, y) in self.path]
|
||
|
||
def get_total_reward(self):
|
||
"""返回总收益"""
|
||
return self.total_value
|
||
|
||
def add_path_to_map(self):
|
||
"""在地图上标记路径"""
|
||
marked_map = [row.copy() for row in self.map_data]
|
||
|
||
# 标记路径点
|
||
for i, (x, y) in enumerate(self.path):
|
||
if marked_map[y][x] == 's':
|
||
marked_map[y][x] = 'S' # 标记起点
|
||
elif marked_map[y][x] == 'e':
|
||
marked_map[y][x] = 'E' # 标记终点
|
||
elif (x, y) in [r['position'] for r in self.collected_resources]:
|
||
marked_map[y][x] = '*' # 标记已收集资源
|
||
else:
|
||
marked_map[y][x] = '.' # 标记路径点
|
||
|
||
self.marked_map = marked_map
|
||
return marked_map
|
||
|
||
|
||
# 使用示例
|
||
def main():
|
||
obj = MazeGenerator(20,'demo.csv',name="龙脊峡谷迷宫")
|
||
obj.generate(
|
||
seed=123,
|
||
boss_count=2,
|
||
traps_range=(5, 10),
|
||
mechanisms_range=(3, 7),
|
||
skill_traps=8
|
||
)
|
||
obj.export_to_csv()
|
||
|
||
map_data = obj.read_csv()
|
||
see = MazeGenerator(1,filename ='demo.csv')
|
||
see.read_from_csv()
|
||
print("=== 原始迷宫 ===")
|
||
see.print_maze()
|
||
|
||
print("\\n" + "="*60)
|
||
print("使用传统贪心算法:")
|
||
print("="*60)
|
||
player = GreedyPlayer(map_data)
|
||
player.find_path()
|
||
see.maze = player.marked_map
|
||
see.print_maze()
|
||
print(f"传统贪心算法总收益: {player.get_total_reward()}")
|
||
|
||
print("\\n" + "="*60)
|
||
print("使用3x3视野贪心算法:")
|
||
print("="*60)
|
||
# 使用新的3x3视野算法
|
||
greedy_3x3 = Greedy3x3ResourceCollector(map_data)
|
||
result = greedy_3x3.run_3x3_greedy_collection()
|
||
|
||
# 显示结果
|
||
see.maze = greedy_3x3.add_path_to_map()
|
||
see.print_maze()
|
||
|
||
print(f"\\n3x3视野算法结果:")
|
||
print(f" 总移动步数: {result['total_moves']}")
|
||
print(f" 收集资源数量: {result['resources_count']}")
|
||
print(f" 资源总价值: {result['total_value']}")
|
||
print(f" 探索位置数: {result['explored_positions']}")
|
||
|
||
print("\\n收集的资源详情:")
|
||
for i, resource in enumerate(result['collected_resources'], 1):
|
||
print(f" {i}. 位置{resource['position']}: {resource['type']} (价值: {resource['value']})")
|
||
|
||
|
||
def demo_3x3_greedy():
|
||
"""演示3x3视野贪心算法"""
|
||
|
||
# 创建一个示例迷宫
|
||
demo_maze = [
|
||
['s', '0', 'g5', '1', 't3'],
|
||
['0', '1', '0', '0', 'g2'],
|
||
['g3', '0', '1', 't2', '0'],
|
||
['0', 't1', '0', '0', 'g4'],
|
||
['1', '0', 'g1', '0', 'e']
|
||
]
|
||
|
||
print("=== 3x3视野贪心算法演示 ===")
|
||
print("迷宫说明:")
|
||
print(" s: 起点, e: 终点")
|
||
print(" g数字: 金币资源 (正收益)")
|
||
print(" t数字: 陷阱资源 (负收益)")
|
||
print(" 0: 可通行路径, 1: 墙壁")
|
||
print("\\n原始迷宫:")
|
||
for row in demo_maze:
|
||
print(' '.join(f"{cell:>2}" for cell in row))
|
||
|
||
# 使用3x3视野贪心算法
|
||
collector = Greedy3x3ResourceCollector(demo_maze)
|
||
result = collector.run_3x3_greedy_collection()
|
||
|
||
# 显示标记后的迷宫
|
||
marked_maze = collector.add_path_to_map()
|
||
print("\\n标记路径后的迷宫:")
|
||
print("S: 起点, E: 终点, *: 已收集资源, .: 路径")
|
||
for row in marked_maze:
|
||
print(' '.join(f"{cell:>2}" for cell in row))
|
||
|
||
print(f"\\n算法结果:")
|
||
print(f" 总移动步数: {result['total_moves']}")
|
||
print(f" 收集资源数量: {result['resources_count']}")
|
||
print(f" 资源总价值: {result['total_value']}")
|
||
|
||
return collector, result
|
||
|
||
|
||
|
||
|
||
if __name__ == "__main__":
|
||
# 运行演示
|
||
print("选择运行模式:")
|
||
print("1. 完整迷宫生成和算法比较")
|
||
print("2. 简单3x3视野算法演示")
|
||
|
||
choice = input("请输入选择 (1 或 2，默认为 2): ").strip()
|
||
|
||
if choice == "1":
|
||
main()
|
||
else:
|
||
demo_3x3_greedy()
|
||
|
||
|
||
|
||
|