def play_deterministic_game(self, starting_intgamestate, thislogits, thisxnode, otherlogits, otherxnode, thisSess, otherSess):
self.input_tensor.fill(0)
black_groups = unionfind()
white_groups = unionfind()
turn = HexColor.BLACK
intgamestate = []
for imove in starting_intgamestate:
black_groups, white_groups = GameCheck.updateUF(intgamestate, black_groups, white_groups,
imove, turn, self.boardsize)
turn = HexColor.EMPTY - turn
intgamestate.append(imove)
game_status = GameCheck.winner(black_groups, white_groups)
empty_points = []
for i in range(self.boardsize * self.boardsize):
if i not in intgamestate:
empty_points.append(i)
aux_player_color=np.random.randint(HexColor.BLACK, HexColor.EMPTY)
assert aux_player_color == 1 or aux_player_color == 2
first_player=turn
while game_status == HexColor.EMPTY:
self.input_tensor.fill(0)
self.input_tensor_builder.set_position_tensors_in_batch(self.input_tensor, 0, intgamestate)
if aux_player_color != turn:
logits_score = thisSess.run(thislogits, feed_dict={thisxnode: self.input_tensor})
else:
logits_score = otherSess.run(otherlogits, feed_dict={otherxnode: self.input_tensor})
if turn == first_player:
logits_score = np.squeeze(logits_score)
best_action=-1
largest_score=0
for action in empty_points:
if best_action == -1:
largest_score = logits_score[action]
best_action = action
elif logits_score[action] > largest_score:
largest_score=logits_score[action]
best_action = action
selected_int_move = best_action
else:
selected_int_move = softmax_selection(logits_score, empty_points)
black_groups, white_groups = GameCheck.updateUF(intgamestate, black_groups, white_groups,
selected_int_move, turn, self.boardsize)
game_status = GameCheck.winner(black_groups, white_groups)
intgamestate.append(selected_int_move)
empty_points.remove(selected_int_move)
turn = HexColor.EMPTY - turn
reward = 0.25 + 1.0/len(intgamestate) if game_status == HexColor.BLACK else -1.0/len(intgamestate) - 0.25
#print('played one game')
return intgamestate, reward
def playonegame(self, sess, logits, boardsize, x_input_node,
starting_intgamestate):
self.input_tensor.fill(0)
black_groups = unionfind()
white_groups = unionfind()
turn = HexColor.BLACK
intgamestate = []
for imove in starting_intgamestate:
black_groups, white_groups = GameCheck.updateUF(
intgamestate, black_groups, white_groups, imove, turn,
boardsize)
turn = HexColor.EMPTY - turn
intgamestate.append(imove)
whoplayedlastmove = HexColor.BLACK if len(
intgamestate) % 2 == 1 else HexColor.WHITE
game_status = GameCheck.winner(black_groups, white_groups)
empty_points = []
for i in range(boardsize * boardsize):
if i not in intgamestate:
empty_points.append(i)
while game_status == HexColor.EMPTY:
self.input_tensor_builder.set_position_tensors_in_batch(
self.input_tensor, 0, intgamestate)
logits_score = sess.run(
logits, feed_dict={x_input_node: self.input_tensor})
selected_int_move = softmax_selection(logits_score, empty_points)
black_groups, white_groups = GameCheck.updateUF(
intgamestate, black_groups, white_groups, selected_int_move,
turn, boardsize)
game_status = GameCheck.winner(black_groups, white_groups)
intgamestate.append(selected_int_move)
empty_points.remove(selected_int_move)
turn = HexColor.EMPTY - turn
reward = 1.0 if game_status == whoplayedlastmove else -1.0
#print('played one game')
return reward
def playbatchgame(self,
sess,
logits,
boardsize,
batchsize,
x_input_node,
topk,
is_adversarial_pg=False):
intmoveseqlist = []
gameresultlist = []
batch_cnt = 0
while batch_cnt < batchsize:
self.input_tensor.fill(0)
black_groups = unionfind()
white_groups = unionfind()
turn = HexColor.BLACK
intgamestate = []
game_status = HexColor.EMPTY
k = np.random.randint(1, 20)
cnt = 0
empty_points = []
for i in range(boardsize * boardsize):
if i not in intgamestate:
empty_points.append(i)
while game_status == HexColor.EMPTY and cnt < k:
self.input_tensor_builder.set_position_tensors_in_batch(
self.input_tensor, 0, intgamestate)
logits_score = sess.run(
logits, feed_dict={x_input_node: self.input_tensor})
selected_int_move = softmax_selection(logits_score,
empty_points,
temperature=5.0)
black_groups, white_groups = GameCheck.updateUF(
intgamestate, black_groups, white_groups,
selected_int_move, turn, boardsize)
game_status = GameCheck.winner(black_groups, white_groups)
intgamestate.append(selected_int_move)
empty_points.remove(selected_int_move)
turn = HexColor.EMPTY - turn
cnt += 1
if game_status != HexColor.EMPTY:
print('wasted!')
continue
intmoveseqlist.append(intgamestate)
if is_adversarial_pg:
self.input_tensor_builder.set_position_tensors_in_batch(
self.input_tensor, 0, intgamestate)
logits_score = sess.run(
logits, feed_dict={x_input_node: self.input_tensor})
logits_score = np.squeeze(logits_score)
top_points = np.argpartition(-logits_score, kth=topk)[:topk]
top_points = top_points.tolist()
for i in top_points:
if i not in empty_points:
top_points.remove(i)
if len(top_points) == 0:
top_points = np.random.choice(empty_points, topk)
min_reward = 2.0
for i in top_points:
intgamestate.append(i)
reward = self.playonegame(
sess,
logits,
boardsize,
x_input_node,
starting_intgamestate=intgamestate)
reward = -reward
min_reward = min(reward, min_reward)
intgamestate.remove(i)
gameresultlist.append(min_reward)
else:
reward = self.playonegame(sess,
logits,
boardsize,
x_input_node,
starting_intgamestate=intgamestate)
gameresultlist.append(reward)
batch_cnt += 1
return intmoveseqlist, gameresultlist
