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tzzzzzzzx 2 years ago
parent dc786654e4
commit e6128c7b00
4 changed files with 107 additions and 128 deletions
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SafariChess_AI.py
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@ -7,112 +7,78 @@ import random
from math import inf from math import inf
import collections import collections
piece_score = {"7": 120, "1": 100, "6": 120, "5": 80, "4": 60,"3": 70,"2": 100} piece_score = {"7": 180, "1": 200, "6": 120, "5": 80, "4": 60, "3": 50, "2": 81}
mouse_score = [[13, 13, 13, 13, 12, 11, 10, 9, 8], mouse_score = [[13, 13, 13, 13, 12, 11, 10, 9, 8],
[25, 20, 15, 13, 12, 12, 10, 9, 8], [25, 20, 15, 18, 16, 13, 10, 9, 8],
[50, 25, 20, 12, 11, 11, 10, 9, 8], [50, 25, 20, 16, 15, 13, 10, 9, 8],
[1919810, 50, 20, 11, 9, 9, 9, 9, 0], [1919810, 50, 20, 11, 9, 9, 9, 9, 0],
[50, 25, 20, 9, 8, 8, 8, 8, 8], [50, 25, 20, 16, 14, 12, 8, 8, 8],
[25, 20, 15, 9, 8, 8, 8, 8, 8], [25, 20, 15, 17, 15, 12, 8, 8, 8],
[11, 11, 10, 8, 8, 8, 8, 8, 8]] [11, 11, 10, 8, 8, 8, 8, 8, 8]]
# eagle_score = [[ 11, 12, 14, 13, 12, 11, 10, 8, # eagle_score = [[11, 12, 14, 13, 12, 11, 10, 8, 8],
# 8], # [15, 15, 14, 14, 0, 0, 0, 8, 8],
# [ 15, 15, 14, 14, 0, 0, 0, 8, # [50, 20, 20, 0, 0, 0, 0, 8, 8],
# 8], # [100000, 50, 20, 13, 12, 11, 10, 8, 0],
# [ 50, 20, 20, 0, 0, 0, 0, 8, # [50, 20, 20, 14, 0, 0, 0, 8, 8],
# 8], # [15, 12, 15, 14, 0, 0, 0, 8, 8],
# [100000, 50, 20, 13, 12, 11, 10, 8, # [11, 12, 14, 13, 12, 11, 10, 8, 8]]
# 0],
# [ 50, 20, 20, 14, 0, 0, 0, 8,
# 8],
# [ 15, 12, 15, 14, 0, 0, 0, 8,
# 8],
# [ 11, 12, 14, 13, 12, 11, 10, 8,
# 8]]
eagle_score = [[14, 13, 10, 12, 14, 13, 10, 8, 7], eagle_score = [[14, 13, 10, 12, 14, 13, 10, 8, 7],
[15, 15, 14, 0, 0, 0, 9, 8, 6], [15, 15, 14, 0, 0, 0, 9, 8, 6],
[50, 20, 20, 0, 0, 0, 7, 5, 4], [50, 20, 20, 0, 0, 0, 7, 5, 4],
[1919810,50,20,13,12,11,10,8,0], [1919810, 50, 20, 14, 13, 11, 10, 8, 0],
[50, 20, 20, 0, 0, 0, 7, 5, 4], [50, 20, 20, 0, 0, 0, 7, 5, 4],
[15, 12, 15, 0, 0, 0, 9, 8, 6], [15, 12, 15, 0, 0, 0, 9, 8, 6],
[14, 12, 10, 13, 14, 13, 10, 8, 7]] [14, 12, 10, 13, 14, 13, 10, 8, 7]]
fox_score = [[ 11, 12, 14, 13, 12, 11, 10, 8, fox_score = [[11, 12, 14, 13, 12, 11, 10, 8, 8],
8], [15, 15, 14, 0, 0, 0, 10, 8, 11],
[ 15, 15, 14, 0, 0, 0, 10, 8, [50, 20, 20, 0, 0, 0, 10, 9, 10],
8], [1919810, 50, 20, 13, 12, 11, 11, 10, 0],
[ 50, 20, 20, 0, 0, 0, 10, 9, [50, 20, 20, 0, 0, 0, 10, 9, 10],
10], [15, 12, 15, 0, 0, 0, 10, 8, 11],
[1919810, 50, 20, 13, 12, 11, 11, 10, [11, 12, 14, 13, 12, 11, 10, 8, 8]]
0],
[ 50, 20, 20, 0, 0, 0, 10, 9, # wolf_score = [[11, 12, 14, 13, 12, 11, 10, 8, 8],
10], # [15, 15, 14, 0, 0, 0, 10, 8, 8],
[ 15, 12, 15, 0, 0, 0, 10, 8, # [50, 20, 20, 0, 0, 0, 10, 9, 10],
8], # [1919810, 50, 20, 13, 12, 11, 11, 10, 0],
[ 11, 12, 14, 13, 12, 11, 10, 8, # [50, 20, 20, 0, 0, 0, 10, 9, 10],
8]] # [15, 12, 15, 0, 0, 0, 10, 8, 8],
# [11, 12, 14, 13, 12, 11, 10, 8, 8]]
wolf_score = [[ 11, 12, 14, 13, 12, 11, 10, 8, wolf_score = [[11, 12, 14, 13, 12, 11, 10, 10, 8],
8], [15, 15, 14, 0, 0, 0, 10, 13, 10],
[ 15, 15, 14, 0, 0, 0, 10, 8, [50, 20, 20, 0, 0, 0, 10, 12, 10],
8], [1919810, 50, 20, 12, 11, 10, 11, 12, 0],
[ 50, 20, 20, 0, 0, 0, 10, 9, [50, 20, 20, 0, 0, 0, 10, 13, 10],
10], [15, 12, 15, 0, 0, 0, 13, 14, 8],
[1919810, 50, 20, 13, 12, 11, 11, 10, [11, 12, 14, 15, 16, 17, 16, 15, 8]]
0],
[ 50, 20, 20, 0, 0, 0, 10, 9, leopard_score = [[11, 12, 14, 13, 12, 11, 10, 8, 8],
10], [15, 15, 14, 0, 0, 0, 10, 8, 8],
[ 15, 12, 15, 0, 0, 0, 10, 8, [50, 20, 20, 0, 0, 0, 10, 9, 10],
8], [1919810, 50, 20, 13, 12, 11, 11, 10, 0],
[ 11, 12, 14, 13, 12, 11, 10, 8, [50, 20, 20, 0, 0, 0, 10, 9, 10],
8]] [15, 12, 15, 0, 0, 0, 10, 8, 8],
[11, 12, 14, 13, 12, 11, 10, 8, 8]]
leopard_score = [[ 11, 12, 14, 13, 12, 11, 10, 8,
8], lion_score = [[20, 20, 18, 15, 12, 11, 14, 12, 5],
[ 15, 15, 14, 0, 0, 0, 10, 8, [40, 25, 30, 0, 0, 0, 16, 12, 12],
8], [50, 40, 30, 0, 0, 0, 16, 12, 12],
[ 50, 20, 20, 0, 0, 0, 10, 9, [1919810, 50, 20, 15, 15, 15, 9, 12, 0],
10], [50, 40, 30, 0, 0, 0, 16, 12, 12],
[1919810, 50, 20, 13, 12, 11, 11, 10, [40, 25, 30, 0, 0, 0, 16, 12, 12],
0], [20, 20, 18, 15, 12, 11, 14, 12, 5]]
[ 50, 20, 20, 0, 0, 0, 10, 9,
10], elephant_score = [[20, 20, 18, 15, 12, 11, 14, 12, 5],
[ 15, 12, 15, 0, 0, 0, 10, 8, [40, 25, 30, 0, 0, 0, 16, 12, 12],
8], [50, 40, 30, 0, 0, 0, 16, 12, 12],
[ 11, 12, 14, 13, 12, 11, 10, 8, [1919810, 50, 20, 15, 15, 15, 9, 12, 0],
8]] [50, 40, 30, 0, 0, 0, 16, 12, 12],
[40, 25, 30, 0, 0, 0, 16, 12, 12],
lion_score = [[ 20, 20, 18, 15, 12, 11, 14, 12, [20, 20, 18, 15, 12, 11, 14, 12, 5]]
5],
[ 40, 25, 30, 0, 0, 0, 16, 12,
12],
[ 50, 40, 30, 0, 0, 0, 16, 12,
12],
[1919810, 50, 20, 15, 15, 15, 9, 12,
0],
[ 50, 40, 30, 0, 0, 0, 16, 12,
12],
[ 40, 25, 30, 0, 0, 0, 16, 12,
12],
[ 20, 20, 18, 15, 12, 11, 14, 12,
5]]
elephant_score = [[ 20, 20, 18, 15, 12, 11, 14, 12,
5],
[ 40, 25, 30, 0, 0, 0, 16, 12,
12],
[ 50, 40, 30, 0, 0, 0, 16, 12,
12],
[1919810, 50, 20, 15, 15, 15, 9, 12,
0],
[ 50, 40, 30, 0, 0, 0, 16, 12,
12],
[ 40, 25, 30, 0, 0, 0, 16, 12,
12],
[ 20, 20, 18, 15, 12, 11, 14, 12,
5]]
piece_position_scores = {"r1": mouse_score, piece_position_scores = {"r1": mouse_score,
"b1": [line[::-1] for line in mouse_score[::-1]], "b1": [line[::-1] for line in mouse_score[::-1]],
@ -130,8 +96,6 @@ piece_position_scores = {"r1": mouse_score,
"b7": [line[::-1] for line in elephant_score[::-1]], "b7": [line[::-1] for line in elephant_score[::-1]],
} }
DEN_CONQUESTED = 10000 DEN_CONQUESTED = 10000
DRAW = 0 DRAW = 0
global DEPTH # =4 global DEPTH # =4
@ -140,6 +104,7 @@ global DEPTH #=4
def findRandomMove(valid_moves): def findRandomMove(valid_moves):
return valid_moves[random.randint(0, len(valid_moves) - 1)] return valid_moves[random.randint(0, len(valid_moves) - 1)]
# is greedy_move function used here? # is greedy_move function used here?
def find_GreadyMove(game_state, valid_moves): def find_GreadyMove(game_state, valid_moves):
turnMultiplier = 1 if game_state.red_to_move else -1 turnMultiplier = 1 if game_state.red_to_move else -1
@ -158,26 +123,37 @@ def find_GreadyMove(game_state, valid_moves):
def scoreMaterial(game_state): # get the score def scoreMaterial(game_state): # get the score
# input: current game_state
score = 0 score = 0
penalty_for_rep = 0 penalty_for_rep = 0
for row in range(len(game_state.board)): for row in range(len(game_state.board)):
for col in range(len(game_state.board[row])): for col in range(len(game_state.board[row])): # 遍历整个棋盘
piece = game_state.board[row][col] piece = game_state.board[row][col]
if piece != "00": if piece != "00":
piece_position_score = 0 piece_position_score = 0
piece_position_score = piece_position_scores[piece][row][col] piece_position_score = piece_position_scores[piece][row][col] # 获得当前位置的得分
if piece_position_scores[piece][row][col] in last_moves: if piece_position_scores[piece][row][col] in last_moves: # 重复判罚
penalty_for_rep += 70 penalty_for_rep += 70
if piece[0] == 'r': if piece[0] == 'r':
score += piece_position_score + piece_score[piece[1]] - penalty_for_rep score += piece_position_score + piece_score[piece[1]] - penalty_for_rep
elif piece[0] == 'b': elif piece[0] == 'b':
score -= piece_position_score + piece_score[piece[1]] - penalty_for_rep score -= piece_position_score + piece_score[piece[1]] - penalty_for_rep # 注意：这个默认没有考虑”淘汰“的可能性？
#待检查：
return score return score
def findMove_NegaMaxAlphaBeta(game_state, valid_moves, depth, DEPTH, alpha, beta, turn_multiplier): def findMove_NegaMaxAlphaBeta(game_state, valid_moves, depth, DEPTH, alpha, beta, turn_multiplier):
# 对于各个参数的理解：
# game_state：当前状态
# valid_moves：可行的行动列表
# depth：当前深度
# DEPTH：限制深度
# alpha：alpha限制值
# beta：beta限制值
# turn_multiplier：NegaMax特性
global next_move global next_move
if depth == 0: if depth == 0:
return turn_multiplier * scoreMaterial(game_state) return turn_multiplier * scoreMaterial(game_state)
@ -241,6 +217,7 @@ def findMove_MiniMaxAlphaBeta(game_state, valid_moves, depth, alpha, beta, turn_
best_action = move best_action = move
return best_action return best_action
def find_BestMove(game_state, valid_moves, depth_p): def find_BestMove(game_state, valid_moves, depth_p):
global next_move global next_move
DEPTH = depth_p DEPTH = depth_p
@ -255,7 +232,8 @@ def find_BestMove(game_state, valid_moves,depth_p):
# print(f"Possible: {i}") # print(f"Possible: {i}")
# for i in ordered_valid_moves: # for i in ordered_valid_moves:
# print(f"New possible: {i}") # print(f"New possible: {i}")
findMove_NegaMaxAlphaBeta(game_state, ordered_valid_moves,depth_p,DEPTH, -DEN_CONQUESTED, DEN_CONQUESTED,1 if game_state.red_to_move else -1) findMove_NegaMaxAlphaBeta(game_state, ordered_valid_moves, depth_p, DEPTH, -DEN_CONQUESTED, DEN_CONQUESTED,
1 if game_state.red_to_move else -1)
last_moves.append(next_move) last_moves.append(next_move)
return next_move return next_move

1
SafariChess_Gamev1.0.py
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@ -250,6 +250,7 @@ def MainMenu():
pg.mixer.init() #mixer的初始化 pg.mixer.init() #mixer的初始化
music = pg.mixer.music.load(file) #载入一个音乐文件用于播放 music = pg.mixer.music.load(file) #载入一个音乐文件用于播放
pg.mixer.music.play(-1) #播放音乐，-1表示循环播放 pg.mixer.music.play(-1) #播放音乐，-1表示循环播放
pg.mixer.music.set_volume(0)
COLOR_INACTIVE = (255, 108, 0) COLOR_INACTIVE = (255, 108, 0)
COLOR_ACTIVE = (255, 208, 0) COLOR_ACTIVE = (255, 208, 0)

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__pycache__/SafariChess_AI.cpython-39.pyc
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__pycache__/SafariChess_backend.cpython-39.pyc
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