def ttentry(self):
"""
Returns game entry
"""
entry = [self.hands[i][j] for i in range(self.numplayers) for j in range(self.numhands)]
entry = entry + [self.nplayer]
return tuple(entry)
def back_to_startstate(self, move):
"""
Checking if move will cause returning to start state - never-ending loop protection
"""
nextstate = self.copy()
nextstate.make_move(move)
hands_min = min([min(nextstate.hands[i]) for i in range(self.numplayers)])
hands_max = max([max(nextstate.hands[i]) for i in range(self.numplayers)])
return hands_min == 1 and hands_max == 1
if __name__ == "__main__":
from easyAI import Negamax, AI_Player, SSS, DUAL
from easyAI.AI.TT import TT
ai_algo_neg = Negamax(4)
ai_algo_sss = SSS(4)
dict_tt = DictTT(32, JSWHashTT())
ai_algo_dual = DUAL(4, tt=TT(dict_tt))
Chopsticks( [AI_Player(ai_algo_neg),AI_Player(ai_algo_dual)]).play() #first player never wins
print '-'*10
print 'Statistics of custom dictionary:'
print 'Calls of hash: ', dict_tt.num_calls
print 'Collisions: ', dict_tt.num_collisions
def init(self, width): # called at start of game
self.start = True
self.width = width
self.ai_algo = DUAL(2)
self.ttt = GomokuGame(
[Human_Player(), AI_Player(self.ai_algo)], width, 2)
#==================================== PARAMETERS =========================================
timeNo = 20 # the no of times the test should run
player1Win = 0
pl1No = "negamax"
pl2No = "rlAgent"
pl1Steps = 4 # the number of steps each algo thinks in advanced
pl2Steps = 4
rlAgent = RLAgent('./models/mix3/train_49500.h5')
aiAlgosPl1 = {
"negamax": Negamax(pl1Steps),
"dual": DUAL(pl1Steps),
"sss": SSS(pl1Steps),
"negamaxRand": NegamaxRand(pl1Steps),
"rlAgent": rlAgent
}
aiAlgosPl2 = {
"negamax": Negamax(pl2Steps),
"dual": DUAL(pl2Steps),
"sss": SSS(pl2Steps),
"negamaxRand": NegamaxRand(pl2Steps),
"rlAgent": rlAgent
}
resultList = []
strategy = Strategy.NORMAL # scoring method
strategyVal = strategy.value
{{ttt.spot_string(j, i)}}
</button>
</td>
{% endfor %}
</tr>
{% endfor %}
</table>
<button type="submit" name="reset">Start Over</button>
</form>
</body>
</html>
'''
app = Flask(__name__)
# ai_algo = Negamax(2 )
ai_algo = DUAL(2, win_score=1000000)
width = 15
def pack(arr):
ret = {}
for x in xrange(0, width):
for y in xrange(0, width):
if arr[x][y] != 0:
ret[(x, y)] = arr[x][y]
return ret
def unpack(dict):
arr = np.zeros((width, width), np.int8)
for x, y in dict:
def create_dual_tt(depth):
ai_algo_dual = DUAL(depth, tt=TT())
return create_benchmark_game(ai_algo_dual)
def create_dual(depth):
ai_algo_dual = DUAL(depth)
return create_benchmark_game(ai_algo_dual)
for j in range(self.numhands)
]
entry = entry + [self.nplayer]
return tuple(entry)
def back_to_startstate(self, move):
"""
Checking if move will cause returning to start state - never-ending loop protection
"""
nextstate = self.copy()
nextstate.make_move(move)
hands_min = min(
[min(nextstate.hands[i]) for i in range(self.numplayers)])
hands_max = max(
[max(nextstate.hands[i]) for i in range(self.numplayers)])
return hands_min == 1 and hands_max == 1
if __name__ == "__main__":
from easyAI import Negamax, AI_Player, SSS, DUAL, MTDbi, MTDf, MTDstep
from easyAI.AI.TT import TT
dict_tt = DictTT(32)
ai_algo_sss = SSS(6, tt=TT(dict_tt)) # SSS algorithm
ai_algo_neg = Negamax(6, tt=TT(dict_tt)) # Negamax algorithm
ai_algo_bi = MTDbi(6, tt=TT(dict_tt)) # MTDbi algorithm
ai_algo_f = MTDf(5, tt=TT(dict_tt)) # MTDf algorithm
ai_algo_step = MTDstep(5, tt=TT(dict_tt)) # MTDstep algorithm
ai_algo_dual = DUAL(4, tt=TT(dict_tt)) # DUAL algorithm
Chopsticks([AI_Player(ai_algo_neg), AI_Player(ai_algo_step)]).play()
dict_tt.print_stats()