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SafariChess
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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, eagle_score = [[14, 13, 10, 12, 14, 13, 10, 8, 7],
# 8], [15, 15, 14, 0, 0, 0, 9, 8, 6],
# [ 15, 12, 15, 14, 0, 0, 0, 8, [50, 20, 20, 0, 0, 0, 7, 5, 4],
# 8], [1919810, 50, 20, 14, 13, 11, 10, 8, 0],
# [ 11, 12, 14, 13, 12, 11, 10, 8, [50, 20, 20, 0, 0, 0, 7, 5, 4],
# 8]] [15, 12, 15, 0, 0, 0, 9, 8, 6],
eagle_score = [[14,13,10,12,14,13,10,8,7], [14, 12, 10, 13, 14, 13, 10, 8, 7]]
[15,15,14,0,0,0,9,8,6],
[50,20,20,0,0,0,7,5,4], fox_score = [[11, 12, 14, 13, 12, 11, 10, 8, 8],
[1919810,50,20,13,12,11,10,8,0], [15, 15, 14, 0, 0, 0, 10, 8, 11],
[50,20,20,0,0,0,7,5,4], [50, 20, 20, 0, 0, 0, 10, 9, 10],
[15,12,15,0,0,0,9,8,6], [1919810, 50, 20, 13, 12, 11, 11, 10, 0],
[14,12,10,13,14,13,10,8,7]] [50, 20, 20, 0, 0, 0, 10, 9, 10],
[15, 12, 15, 0, 0, 0, 10, 8, 11],
fox_score = [[ 11, 12, 14, 13, 12, 11, 10, 8, [11, 12, 14, 13, 12, 11, 10, 8, 8]]
8],
[ 15, 15, 14, 0, 0, 0, 10, 8, # wolf_score = [[11, 12, 14, 13, 12, 11, 10, 8, 8],
8], # [15, 15, 14, 0, 0, 0, 10, 8, 8],
[ 50, 20, 20, 0, 0, 0, 10, 9, # [50, 20, 20, 0, 0, 0, 10, 9, 10],
10], # [1919810, 50, 20, 13, 12, 11, 11, 10, 0],
[1919810, 50, 20, 13, 12, 11, 11, 10, # [50, 20, 20, 0, 0, 0, 10, 9, 10],
0], # [15, 12, 15, 0, 0, 0, 10, 8, 8],
[ 50, 20, 20, 0, 0, 0, 10, 9, # [11, 12, 14, 13, 12, 11, 10, 8, 8]]
10], wolf_score = [[11, 12, 14, 13, 12, 11, 10, 10, 8],
[ 15, 12, 15, 0, 0, 0, 10, 8, [15, 15, 14, 0, 0, 0, 10, 13, 10],
8], [50, 20, 20, 0, 0, 0, 10, 12, 10],
[ 11, 12, 14, 13, 12, 11, 10, 8, [1919810, 50, 20, 12, 11, 10, 11, 12, 0],
8]] [50, 20, 20, 0, 0, 0, 10, 13, 10],
[15, 12, 15, 0, 0, 0, 13, 14, 8],
wolf_score = [[ 11, 12, 14, 13, 12, 11, 10, 8, [11, 12, 14, 15, 16, 17, 16, 15, 8]]
8],
[ 15, 15, 14, 0, 0, 0, 10, 8, leopard_score = [[11, 12, 14, 13, 12, 11, 10, 8, 8],
8], [15, 15, 14, 0, 0, 0, 10, 8, 8],
[ 50, 20, 20, 0, 0, 0, 10, 9, [50, 20, 20, 0, 0, 0, 10, 9, 10],
10], [1919810, 50, 20, 13, 12, 11, 11, 10, 0],
[1919810, 50, 20, 13, 12, 11, 11, 10, [50, 20, 20, 0, 0, 0, 10, 9, 10],
0], [15, 12, 15, 0, 0, 0, 10, 8, 8],
[ 50, 20, 20, 0, 0, 0, 10, 9, [11, 12, 14, 13, 12, 11, 10, 8, 8]]
10],
[ 15, 12, 15, 0, 0, 0, 10, 8, lion_score = [[20, 20, 18, 15, 12, 11, 14, 12, 5],
8], [40, 25, 30, 0, 0, 0, 16, 12, 12],
[ 11, 12, 14, 13, 12, 11, 10, 8, [50, 40, 30, 0, 0, 0, 16, 12, 12],
8]] [1919810, 50, 20, 15, 15, 15, 9, 12, 0],
[50, 40, 30, 0, 0, 0, 16, 12, 12],
leopard_score = [[ 11, 12, 14, 13, 12, 11, 10, 8, [40, 25, 30, 0, 0, 0, 16, 12, 12],
8], [20, 20, 18, 15, 12, 11, 14, 12, 5]]
[ 15, 15, 14, 0, 0, 0, 10, 8,
8], elephant_score = [[20, 20, 18, 15, 12, 11, 14, 12, 5],
[ 50, 20, 20, 0, 0, 0, 10, 9, [40, 25, 30, 0, 0, 0, 16, 12, 12],
10], [50, 40, 30, 0, 0, 0, 16, 12, 12],
[1919810, 50, 20, 13, 12, 11, 11, 10, [1919810, 50, 20, 15, 15, 15, 9, 12, 0],
0], [50, 40, 30, 0, 0, 0, 16, 12, 12],
[ 50, 20, 20, 0, 0, 0, 10, 9, [40, 25, 30, 0, 0, 0, 16, 12, 12],
10], [20, 20, 18, 15, 12, 11, 14, 12, 5]]
[ 15, 12, 15, 0, 0, 0, 10, 8,
8],
[ 11, 12, 14, 13, 12, 11, 10, 8,
8]]
lion_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]]
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]],
@ -130,17 +96,16 @@ 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
DRAW = 0
DEN_CONQUESTED=10000 global DEPTH # =4
DRAW=0
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
maxScore = -DEN_CONQUESTED maxScore = -DEN_CONQUESTED
@ -157,27 +122,38 @@ def find_GreadyMove(game_state, valid_moves):
return bestMove return bestMove
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)
@ -186,16 +162,16 @@ def findMove_NegaMaxAlphaBeta(game_state, valid_moves, depth,DEPTH, alpha, beta,
for move in valid_moves: for move in valid_moves:
game_state.makeMove(move) game_state.makeMove(move)
next_moves = game_state.getAllMoves() next_moves = game_state.getAllMoves()
score = -findMove_NegaMaxAlphaBeta(game_state, next_moves, depth - 1,DEPTH, -beta, -alpha, -turn_multiplier) score = -findMove_NegaMaxAlphaBeta(game_state, next_moves, depth - 1, DEPTH, -beta, -alpha, -turn_multiplier)
if score > max_score: # > or >= ?? if score > max_score: # > or >= ??
max_score = score max_score = score
if depth == DEPTH: if depth == DEPTH:
next_move = move next_move = move
game_state.undoMove() game_state.undoMove()
if max_score > alpha: if max_score > alpha:
alpha = max_score alpha = max_score
if alpha >= beta: if alpha >= beta:
break break
return max_score return max_score
@ -204,26 +180,26 @@ def findMove_NegaMaxAlphaBeta(game_state, valid_moves, depth,DEPTH, alpha, beta,
def findMove_MiniMaxAlphaBeta(game_state, valid_moves, depth, alpha, beta, turn_multiplier): def findMove_MiniMaxAlphaBeta(game_state, valid_moves, depth, alpha, beta, turn_multiplier):
global next_move global next_move
def max_value(game_state,next_moves, alpha, beta,depth): def max_value(game_state, next_moves, alpha, beta, depth):
if depth == 0: if depth == 0:
return turn_multiplier * scoreMaterial(game_state) return turn_multiplier * scoreMaterial(game_state)
v = -inf v = -inf
for move in valid_moves: for move in valid_moves:
next_moves = game_state.getAllMoves() next_moves = game_state.getAllMoves()
v = max(v, min_value(game_state,next_moves, alpha, beta,depth - 1)) v = max(v, min_value(game_state, next_moves, alpha, beta, depth - 1))
game_state.undoMove() game_state.undoMove()
if v >= beta: if v >= beta:
return v return v
alpha = max(alpha, v) alpha = max(alpha, v)
return v return v
def min_value(game_state,next_moves, alpha, beta,depth): def min_value(game_state, next_moves, alpha, beta, depth):
if depth == 0: if depth == 0:
return turn_multiplier * scoreMaterial(game_state) return turn_multiplier * scoreMaterial(game_state)
v = -inf v = -inf
for move in valid_moves: for move in valid_moves:
next_moves = game_state.getAllMoves() next_moves = game_state.getAllMoves()
v = min(v, max_value(game_state,next_moves, alpha, beta,depth - 1)) v = min(v, max_value(game_state, next_moves, alpha, beta, depth - 1))
game_state.undoMove() game_state.undoMove()
if v <= alpha: if v <= alpha:
return v return v
@ -235,27 +211,29 @@ def findMove_MiniMaxAlphaBeta(game_state, valid_moves, depth, alpha, beta, turn_
beta = inf beta = inf
best_action = None best_action = None
for move in valid_moves: for move in valid_moves:
v = min_value(game_state,valid_moves, best_score, beta,depth) v = min_value(game_state, valid_moves, best_score, beta, depth)
if v > best_score: if v > best_score:
best_score = v best_score = v
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
next_move = None next_move = None
global last_moves global last_moves
last_moves = collections.deque(maxlen=12) last_moves = collections.deque(maxlen=12)
random.shuffle(valid_moves) random.shuffle(valid_moves)
ordered_valid_moves=orderby_GreadyMove(game_state, valid_moves) ordered_valid_moves = orderby_GreadyMove(game_state, valid_moves)
# for i in valid_moves: # for i in valid_moves:
# 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

3
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)
@ -774,7 +775,7 @@ if __name__ == '__main__':
#print("Loading...") #print("Loading...")
#print("Initialing game...") #print("Initialing game...")
mode = MainMenu() mode = MainMenu()
#mode = 4 # mode = 4
main(mode) main(mode)

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