def __init__(self, player_no: int, max_depth: int):
super(MinMaxAgent, self).__init__(player_no)
self.max_depth = max_depth
self.zb = ZobristHash(player_no)
self.squares = [[1], [3], [5], [7], [8], [10], [12], [14], [17], [19],
[21], [23], [24], [26], [28], [30], [33], [35], [37],
[39], [40], [42], [44], [46], [49], [51], [53], [55],
[56], [58], [60], [62]]
def __init__(self, name, color):
# print "In ChessAI __init__"
self.name = name
self.color = color
self.type = 'AI'
self.table = ZobristHash(size=2**24)
class ChessAI:
def __init__(self, name, color):
# print "In ChessAI __init__"
self.name = name
self.color = color
self.type = 'AI'
self.table = ZobristHash(size=2**24)
def GetName(self):
return self.name
def GetColor(self):
return self.color
def GetType(self):
return self.type
def GetMove(self, currentNode, depth):
actions = currentNode.Actions("black", self.table)
bestMoveTuple = None
bestMoveUtility = self.AlphaBetaSearch(currentNode=currentNode, depth=depth, actions=actions)
#get the best move tuple
for i in actions:
if i.utility == bestMoveUtility:
bestMoveTuple = i.GetMoveTuple()
break
return bestMoveTuple
def AlphaBetaSearch(self, alpha=-10000, beta=10000, currentNode=None, maxPlayer=True, depth=0, actions=None):
#use hashtable
cachedValue = self.table.lookup(currentNode.board)
if cachedValue != None:
currentNode.SetUtility(cachedValue) #utility
return cachedValue
#terminal test1
if depth == 0:
Heuristic.ShannonHeuristic(currentNode, self.table)
return currentNode.utility
if actions == None:
if maxPlayer:
actions = currentNode.Actions("black", self.table)
else:
actions = currentNode.Actions("white", self.table)
#terminal test2
if len(actions) == 0:
Heuristic.ShannonHeuristic(currentNode, self.table)
return currentNode.utility
# Max
if maxPlayer:
v = -10000
for node in actions:
v = max(v, self.AlphaBetaSearch( alpha, beta, node, False, depth-1 ) )
if v >= beta:
return v
alpha = max(alpha, v)
self.table.insertUtility(currentNode.board, v)
currentNode.SetUtility(v)
return v
# Min
else:
v = 10000
for node in actions:
v = min(v, self.AlphaBetaSearch( alpha, beta, node, True, depth-1 ) )
if v <= alpha:
return v
beta = max( beta, v)
self.table.insertUtility(currentNode.board, v)
currentNode.SetUtility(v)
return v
class MinMaxAgent(Agent):
def __init__(self, player_no: int, max_depth: int):
super(MinMaxAgent, self).__init__(player_no)
self.max_depth = max_depth
self.zb = ZobristHash(player_no)
self.squares = [[1], [3], [5], [7], [8], [10], [12], [14], [17], [19],
[21], [23], [24], [26], [28], [30], [33], [35], [37],
[39], [40], [42], [44], [46], [49], [51], [53], [55],
[56], [58], [60], [62]]
def play(self, moves: list, board: list):
self.z_hash = {}
_, move_index = self._minmax_ab(self.max_depth, -math.inf, math.inf,
bool(self.player_no), board,
self.player_no)
return moves[move_index]
def _minmax_ab(self, depth, alpha, beta, max_player, board, player_no):
if player_no:
steps = [-7, -9]
player_num = 1
else:
steps = [9, 7]
player_num = -1
moves = Board.get_valid_moves(board, self.squares, steps, player_num,
False, False)[::-1]
length = len(moves)
if depth == 0 or length == 0:
return self._get_board_hash_score(board, player_no), 0
if max_player:
max_eval = -math.inf
for i in range(length):
next_board = [i for i in board]
next_board = Board.play_move(next_board, moves[i], player_no)
hash = self.zb.calculate_hash(next_board, player_no)
if hash in self.z_hash:
eval = self.z_hash[hash]
else:
eval, _ = self._minmax_ab(depth - 1, alpha, beta, False,
next_board, 1 - player_no)
if eval > max_eval:
max_index = i
max_eval = eval
alpha = max(eval, alpha)
if beta <= alpha:
break
return max_eval, (length - 1) - max_index
else:
min_eval = math.inf
for i in range(length):
next_board = [i for i in board]
next_board = Board.play_move(next_board, moves[i], player_no)
hash = self.zb.calculate_hash(next_board, player_no)
if hash in self.z_hash:
eval = self.z_hash[hash]
else:
eval, _ = self._minmax_ab(depth - 1, alpha, beta, True,
next_board, 1 - player_no)
if eval < min_eval:
min_eval = eval
min_index = i
min_eval = min(min_eval, eval)
beta = min(eval, beta)
if beta <= alpha:
break
return min_eval, (length - 1) - min_index
def _get_board_hash_score(self, board, player_no):
hash = self.zb.calculate_hash(board, player_no)
if hash not in self.z_hash:
score = self._heuristicFunction(board)
self.z_hash[hash] = score
else:
score = self.z_hash[hash]
return score
def _heuristicFunction(self, board: list) -> int:
counter = collections.Counter(board)
if -1 not in counter and -2 not in counter:
return 72
elif 1 not in counter and 2 not in counter:
return -72
return sum(board)
