def getScore(self, weights, actionSequence, bestScore, bestSequence):
#print "BEST SEQUENCE", bestSequence
original_score = self.score
#Maybe need to copy
original_board = copy.deepcopy(self.board)
original_stone = self.stone
original_stone_x = self.stone_x
original_stone_y = self.stone_y
original_lines = self.lines
#print "original"
#print original_board
for move in actionSequence:
self.key_actions[move]()
self.key_actions["RETURN"]()
features = tetrisai.extractFeatures(self.board, self.next_stone)
score = self.test_evaluation_function(weights, features)
# After trying the move, we have to revert everything
self.score = original_score
self.board = original_board
self.stone = original_stone
self.stone_x = original_stone_x
self.stone_y = original_stone_y
self.lines = original_lines
#print "new board"
#print self.board
return score
def train_evaluation_function(self):
# EDIT THIS LATER!!!
lenFeatures = len(tetrisai.extractFeatures(self.board, self.stone))
weights = [0] * lenFeatures
numGames = 30
for i in range(numGames):
print "GAME", i
weights = self.td_learning(weights)
#weights = td_learning(self, weights)
return weights
def run(self):
self.gameover = False
self.paused = False
dont_burn_my_cpu = pygame.time.Clock()
while 1:
self.screen.fill((0, 0, 0))
if self.gameover:
self.center_msg("""Game Over!\nYour score: %d
Press space to continue""" % self.score)
else:
if self.paused:
self.center_msg("Paused")
else:
pygame.draw.line(self.screen, (255, 255, 255),
(self.rlim + 1, 0),
(self.rlim + 1, self.height - 1))
self.disp_msg("Next:", (self.rlim + cell_size, 2))
self.disp_msg(
"Score: %d\n\nLevel: %d\
\nLines: %d" % (self.score, self.level, self.lines),
(self.rlim + cell_size, cell_size * 5))
self.draw_matrix(self.bground_grid, (0, 0))
self.draw_matrix(self.board, (0, 0))
self.draw_matrix(self.stone, (self.stone_x, self.stone_y))
self.draw_matrix(self.next_stone, (cols + 1, 2))
pygame.display.update()
for event in pygame.event.get():
print "[numBlocks, totalBlockWeight, bumpiness, maxHeight, minHeight, meanHeight, varianceHeight, maxHoleHeight, numHoles, density, numRowsWithHoles, numColsWithHoles]"
print tetrisai.extractFeatures(self.board, self.stone)
if event.type == pygame.USEREVENT + 1:
self.drop(False)
elif event.type == pygame.QUIT:
self.quit()
elif event.type == pygame.KEYDOWN:
for key in self.key_actions:
if event.key == eval("pygame.K_" + key):
self.key_actions[key]()
dont_burn_my_cpu.tick(maxfps)
def trySequence(self, weights, actionSequence, bestScore, bestSequence):
#print "BEST SEQUENCE", bestSequence
original_score = self.score
#Maybe need to copy
original_board = copy.deepcopy(self.board)
original_stone = self.stone
original_stone_x = self.stone_x
original_stone_y = self.stone_y
original_lines = self.lines
#print "original"
#print original_board
for move in actionSequence:
self.key_actions[move]()
self.key_actions["RETURN"]()
features = tetrisai.extractFeatures(self.board, self.next_stone)
#print "Features for attempt",
#print features
score = self.test_evaluation_function(weights, features)
#print "attempted: sequence", actionSequence, "score", score
#print "resulting board"
#print self.board
# After trying the move, we have to revert everything
self.score = original_score
self.board = original_board
self.stone = original_stone
self.stone_x = original_stone_x
self.stone_y = original_stone_y
self.lines = original_lines
#print "new board"
#print self.board
if score < bestScore:
return score, list(actionSequence)
else:
return bestScore, bestSequence
def trySequence2(self, cutoff, weights, depth, actionSequence, bestScore,
bestSequence):
# Cache all old values
original_score = self.score
original_board = copy.deepcopy(self.board)
original_stone = copy.deepcopy(self.stone)
original_stone_x = self.stone_x
original_stone_y = self.stone_y
original_lines = self.lines
original_next_stone = copy.deepcopy(self.next_stone)
#print "original"
#print original_board
for move in actionSequence:
self.key_actions[move]()
self.key_actions["RETURN"]()
features = tetrisai.extractFeatures(self.board, self.next_stone)
score = self.test_evaluation_function(weights, features)
#print "Features for attempt",
#print features
if (depth > 1 and score > cutoff):
range_rotations = 3
range_left = 5
range_right = 6
#range_left = self.stone_x
#range_right = len(self.board[0]) - (self.stone_x + len(self.stone[0])) + 1
# Find best action sequence and its corresponding evaluation functino score
nextBestScore = 999999999999999
nextBestSequence = []
nextActionSequence = []
nextBestScore, nextBestSequence = self.trySequence2(
cutoff, weights, depth - 1, nextActionSequence, nextBestScore,
nextBestSequence)
for i in range(range_rotations + 1):
nextBestScore, nextBestSequence = self.trySequence2(
cutoff, weights, depth - 1, nextActionSequence,
nextBestScore, nextBestSequence)
for j in range(range_left):
nextActionSequence.append("LEFT")
nextBestScore, nextBestSequence = self.trySequence2(
cutoff, weights, depth - 1, nextActionSequence,
nextBestScore, nextBestSequence)
nextActionSequence = nextActionSequence[0:len(actionSequence) -
range_left]
for j in range(range_right):
nextActionSequence.append("RIGHT")
nextBestScore, nextBestSequence = self.trySequence2(
cutoff, weights, depth - 1, nextActionSequence,
nextBestScore, nextBestSequence)
nextActionSequence = nextActionSequence[0:len(actionSequence) -
range_right]
nextActionSequence.append("UP")
nextBestSequence.append("RETURN")
score += nextBestScore
# After trying the move sequence, we have to revert everything
self.score = original_score
self.board = original_board
self.stone = original_stone
self.stone_x = original_stone_x
self.stone_y = original_stone_y
self.lines = original_lines
self.next_stone = original_next_stone
if score < bestScore:
return score, list(actionSequence)
else:
return bestScore, bestSequence
def td_learning(self, weights):
self.board = new_board()
print self.board
'''
pygame.init()
self.width = cell_size*(cols+6)
self.height = cell_size*rows
self.rlim = cell_size*cols
self.init_game()
'''
self.gameover = False
self.paused = False
while not self.gameover:
self.stone = self.next_stone
self.stone_x = int(cols / 2 - len(self.stone[0]) / 2)
self.stone_y = 0
if (check_collision(self.board, self.stone,
(self.stone_x, self.stone_y))):
self.gameover = True
return weights
features = tetrisai.extractFeatures(self.board, self.stone)
eta = 0.01
discount = 1
prev_dot_product = 0
for i in range(len(weights)):
prev_dot_product += weights[i] * features[i]
prev_score = self.score
prev_lines = self.lines
moves = baseline.findBestMove(self.board, self.stone, self.stone_x)
for move in moves:
self.key_actions[move]()
'''
# Random number of rotations
numRotations = random.randint(0,3)
for i in range(numRotations):
key_actions["UP"]()
# Random left right translation
if random.randint(0,1) == 0:
range_left = self.stone_x
for i in range(random.randint(0,range_left)):
key_actions['LEFT']()
else:
range_right = len(self.board[0]) - (self.stone_x + len(self.stone[0]))
for i in range(random.randint(0,range_right)):
key_actions['RIGHT']()
key_actions['RETURN']() '''
new_score = self.score
#score_reward = new_score - prev_score
reward = 10000 * (self.lines - prev_lines)**2
if self.gameover:
print "GESSSS HERE"
reward = -1000
self.next_stone = tetris_shapes[rand(len(tetris_shapes))]
newFeatures = tetrisai.extractFeatures(self.board, self.next_stone)
new_dot_product = 0
for i in range(len(weights)):
new_dot_product += weights[i] * newFeatures[i]
print prev_dot_product, new_dot_product
for index, weight in enumerate(weights):
weights[index] = weight - eta * (
prev_dot_product -
(reward + discount * new_dot_product)) * features[index]
sumWeights = sum(weights) + .01
weights = [weight * 1.0 / sumWeights for weight in weights]
print "Features", features
print "WEIGHTS", weights
return weights