def getBestMove(node, bestVal):
bestChildren = []
for child in node.children:
if child.value == bestVal:
bestChildren.append(child)
if len(bestChildren) > 0:
index = np.random.randint(0, len(bestChildren))
bestNode = bestChildren[index]
else:
# print("Error, best Move couldn't find. Randomly selects the move!")
moves = HexBoard.getMoveList(node)
index = np.random.randint(0, len(moves))
return moves[index]
b_size = node.board.shape[0]
for i in range(b_size):
for j in range(b_size):
if node.board[i, j] != bestNode.board[i, j]:
return i, j
def alphaBetaSearchDijkstra(self, node, depth, alpha, beta, isMaximizer):
node['searched'] = True
if depth == 0 or node['type'] == 'LEAF':
# if depth == 0, then it is already a leaf node
node['type'] = 'LEAF'
#node['value'] = self.dummyEval()
# calculate the value
if isMaximizer:
node['value'] = self.dijkstraEval(node['board'], 1)
else:
node['value'] = self.dijkstraEval(node['board'], 2)
return node['value']
# If we already get this nodes children then this section we be skipped
if len(node['children']) <= 0:
# get children of this node
children = []
moves = HexBoard.getMoveList(node)
# If there is no possible move then this is a leaf node
if len(moves) == 0:
# Node type is changing!!!!
node['type'] = 'LEAF'
if isMaximizer:
node['value'] = self.dijkstraEval(node['board'], 1)
else:
node['value'] = self.dijkstraEval(node['board'], 2)
return node['value']
else:
for move in moves:
b = node['board'].copy()
if isMaximizer:
b[move] = self.MAXIMIZER
else:
b[move] = self.MINIMIZER
child_id = str(uuid.uuid4())
node_type = 'MIN'
if node['type'] == 'MIN':
node_type = 'MAX'
child = {
'id': child_id,
'type': node_type,
'children': [],
'parent_type': node['type'],
'searched': False,
'board': b,
'value': None,
'name': child_id[-3:]
}
children.append(child)
node['children'] = children
if isMaximizer:
bestVal = -self.INF
for n in node['children']:
bestVal = max(
bestVal,
self.alphaBetaSearchDijkstra(n, depth - 1, alpha, beta,
False))
alpha = max(alpha, bestVal) # Updating alpha
if bestVal >= beta:
break # beta cutoff, a>=b
else:
bestVal = self.INF
for n in node['children']:
bestVal = min(
bestVal,
self.alphaBetaSearchDijkstra(n, depth - 1, alpha, beta,
True))
beta = min(beta, bestVal) # Updating beta
if alpha >= bestVal:
break # alpha cutoff, a>=b
node['value'] = bestVal
return bestVal
def alphaBetaSearchDijkstra(self, node, depth, alpha, beta, isMaximizer):
node.searched = True
if depth == 0 or node.type == 'LEAF':
# calculate the value
if isMaximizer:
node.value = self.dijkstraEval(node.board, self.MAXIMIZER)
else:
node.value = self.dijkstraEval(node.board, self.MINIMIZER)
return node.value
# If we already get this nodes children then this section will be skipped
if len(node.children) <= 0:
# get children of this node
children = []
moves = HexBoard.getMoveList(node)
# If there is no possible move then this is a leaf node
if len(moves) == 0:
if isMaximizer:
node.value = self.dijkstraEval(node.board, self.MAXIMIZER)
else:
node.value = self.dijkstraEval(node.board, self.MINIMIZER)
return node.value
else:
for move in moves:
b = node.board.copy()
if isMaximizer:
b[move] = self.MAXIMIZER
else:
b[move] = self.MINIMIZER
child_type = 'MIN'
if node.type == 'MIN':
child_type = 'MAX'
child = Node(node_type=child_type,
board_state=b,
parent=node)
children.append(child)
node.children = children
# Which one is the first player?
if isMaximizer:
bestVal = -self.INF
for n in node.children:
bestVal = max(
bestVal,
self.alphaBetaSearchDijkstra(n, depth - 1, alpha, beta,
False))
alpha = max(alpha, bestVal) # Updating alpha
if bestVal >= beta:
break # beta cutoff, a>=b
else:
bestVal = self.INF
for n in node.children:
bestVal = min(
bestVal,
self.alphaBetaSearchDijkstra(n, depth - 1, alpha, beta,
True))
beta = min(beta, bestVal) # Updating beta
if alpha >= bestVal:
break # alpha cutoff, a>=b
node.value = bestVal
return bestVal
