/
synapsemon_arthur_white_mini_140.py
135 lines (113 loc) · 4.19 KB
/
synapsemon_arthur_white_mini_140.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
#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_arthur_white_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_arthur_white_mini_140.xml')
NetworkWriter.writeToFile(net, 'CheckersMini/synapsemon_arthur_white_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_arthur_white_mini_140.xml')
NetworkWriter.writeToFile(net, 'CheckersMini/synapsemon_arthur_white_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