def monte_play(game_state): ans=0.0 numRuns = 10 if(game_state.maxValue() >= 16): numRuns = 20 elif(game_state.maxValue() >= 32): numRuns = 50 elif(game_state.maxValue() >= 256): numRuns = 75 elif(game_state.maxValue() >= 512): numRuns = 120 for i in range(numRuns): # print(i) tmp=TwentyFortyEight() tmp.cells=game_state.cells tmp.score=game_state.score count = 0 while(tmp.canMove()): # count += 1 # print(count) dir=random.choice([1,2,3,4]) tmp.move(dir) tmp.new_tile() #print(game_state.cells) ans+=tmp.score1() ans /= numRuns return ans
def run_2048_new_tile_test():
"""
Checks if new_tile() creates a new tile of either number 2 with 90%
possibility or a 4 with 10% possibility.
"""
twos = 0
fours = 0
NUM_COUNTS = 1000
for count in range(NUM_COUNTS):
o_game_board = TwentyFortyEight(2, 2)
o_game_board.new_tile()
twos += sum([row.count(2) for row in o_game_board.arr_grid])
fours += sum([row.count(4) for row in o_game_board.arr_grid])
print "twos: %d / %d - %4.1f percent" % (twos, NUM_COUNTS, (twos * 100.0) / NUM_COUNTS)
print "fours: %d / %d - %4.1f percent" % (fours, NUM_COUNTS, (fours * 100.0) / NUM_COUNTS)
class Environment(object):
def __init__(self):
self.S = TwentyFortyEight()
self.S.make_tables()
self.score = 0
self.S.new_tile()
print("New Episode")
def reset(self):
self.S.score = 0
self.S.cells = 0
self.score = 0
self.S.new_tile()
return self.S.vectorize_state()
def step(self, action):
score_prev = self.S.score1()
cells = self.S.cells
self.S.move(action + 1)
r = self.S.score1() - score_prev
self.score = self.S.score1()
if not cells == self.S.cells:
self.S.new_tile()
if not self.S.canMove():
return self.S.vectorize_state(), r, True
return self.S.vectorize_state(), r, False
def seed(self, a):
return
# x.new_tile()
# # print("GAME ENDs")
# # print(x.maxValue())
# print("Score:"+str(x.score)+"\t Max Tile:"+str(x.maxValue()))
# # x.__str__()
# # print(x.get_available_moves())
occ = np.zeros(16)
for i in range(500):
x=TwentyFortyEight()
x.make_tables()
# x.print_tables()
# print("Generated Tables")
x.new_tile()
# avail_moves = x.get_available_moves()
while(True):
# x.__str__()
# print("-----------------------")
temp = x.vectorize_state();
tempx = np.zeros((1, 256))
for j in range(16):
tempx[0, j*16 + temp[j]] = 1
probs=y.eval(feed_dict={x1: tempx})[0]
# print(probs)
# dir = eminimax(x,2)
# dir=minimax_alpha_beta(x,6)
# dir=monte_carlo(x)
avail_moves = x.get_available_moves()
