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synapsemon_self_black_mini_140.py
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synapsemon_self_black_mini_140.py
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#A checkers AI implementation based on TD Backgammon
from pybrain.tools.customxml.networkreader import NetworkReader
from pybrain.tools.customxml.networkwriter import NetworkWriter
from pybrain.datasets import SupervisedDataSet
from pybrain.supervised.trainers import BackpropTrainer
# Constants
BLACK, WHITE = 0, 1
#neural net
net = NetworkReader.readFrom('CheckersMini/synapsemon_self_black_mini_140.xml')
def move_function(board):
global net
best_max_move = None
max_value = -1000
best_min_move = None
min_value = 1000
#value is the chance of black winning
for m in board.get_moves():
nextboard = board.peek_move(m)
value = net.activate(board_to_input(nextboard))
if value > max_value:
max_value = value
best_max_move = m
if value < min_value:
min_value = value
best_min_move = m
ds = SupervisedDataSet(97, 1)
best_move = None
#active player
if board.active == BLACK:
ds.addSample(board_to_input(board), max_value)
best_move = best_max_move
elif board.active == WHITE:
ds.addSample(board_to_input(board), min_value)
best_move = best_min_move
trainer = BackpropTrainer(net, ds)
trainer.train()
NetworkWriter.writeToFile(net, 'CheckersMini/synapsemon_self_black_mini_140.xml')
NetworkWriter.writeToFile(net, 'CheckersMini/synapsemon_self_black_mini_140_copy.xml')
return best_move
def end_function(board, lose):
global net
ds = SupervisedDataSet(97, 1)
if lose:
if board.active == BLACK:
ds.addSample(board_to_input(board), 0)
whiteboard = board_to_input(board)
whiteboard[96] = 0
ds.addSample(whiteboard, 1)
elif board.active == WHITE:
ds.addSample(board_to_input(board), 1)
blackboard = board_to_input(board)
blackboard[96] = 1
ds.addSample(blackboard, 0)
else:
#black loses
if board.active == BLACK:
ds.addSample(board_to_input(board), 0)
whiteboard = board_to_input(board)
whiteboard[96] = 0
ds.addSample(whiteboard, 0)
#black wins
elif board.active == WHITE:
ds.addSample(board_to_input(board), 1)
blackboard = board_to_input(board)
blackboard[96] = 1
ds.addSample(blackboard, 1)
trainer = BackpropTrainer(net, ds)
trainer.train()
NetworkWriter.writeToFile(net, 'CheckersMini/synapsemon_self_black_mini_140.xml')
NetworkWriter.writeToFile(net, 'CheckersMini/synapsemon_self_black_mini_140_copy.xml')
def board_to_input(board):
EMPTY = -1
BLACK_KING = 2
WHITE_KING = 3
if board.active == BLACK:
black_kings = board.backward[board.active]
black_men = board.forward[board.active] ^ black_kings
white_kings = board.forward[board.passive]
white_men = board.backward[board.passive] ^ white_kings
else:
black_kings = board.backward[board.passive]
black_men = board.forward[board.passive] ^ black_kings
white_kings = board.forward[board.active]
white_men = board.backward[board.active] ^ white_kings
state = [[None for _ in range(8)] for _ in range(4)]
for i in range(4):
for j in range(8):
cell = 1 << (9*i + j)
if cell & black_men:
state[i][j] = BLACK
elif cell & white_men:
state[i][j] = WHITE
elif cell & black_kings:
state[i][j] = BLACK_KING
elif cell & white_kings:
state[i][j] = WHITE_KING
else:
state[i][j] = EMPTY
#flatten list
state = [item for sublist in state for item in sublist]
inpt = [0] * 97
for i in range(32):
if state[i] == BLACK or state[i] == BLACK_KING:
inpt[i] = 1
elif state[i] == WHITE or state[i] == WHITE_KING:
inpt[i + 32] = 1
if state[i] == BLACK_KING or state[i] == WHITE_KING:
inpt[i + 64] = 1
if board.active == BLACK:
inpt[96] = 1
else:
inpt[96] = 0
return inpt