def test_done():
import cchess_alphazero.environment.static_env as senv
state = '1Rems1e1r/4m4/2c6/p3C1p1p/9/6P2/P3P3P/1c5C1/3p5/2EMSME1R'
board = senv.state_to_board(state)
for i in range(9, -1, -1):
print(board[i])
print(senv.done(state))
def start_game(self, idx):
pipe1 = self.pipes_bt.pop()
pipe2 = self.pipes_ng.pop()
search_tree1 = defaultdict(VisitState)
search_tree2 = defaultdict(VisitState)
self.player1 = CChessPlayer(self.config, search_tree=search_tree1, pipes=pipe1,
debugging=False, enable_resign=True)
self.player2 = CChessPlayer(self.config, search_tree=search_tree2, pipes=pipe2,
debugging=False, enable_resign=True)
# even: bst = red, ng = black; odd: bst = black, ng = red
if idx % 2 == 0:
red = self.player1
black = self.player2
logger.debug(f"best model is red, ng is black")
else:
red = self.player2
black = self.player1
logger.debug(f"best model is black, ng is red")
state = senv.INIT_STATE
value = 0 # best model's value
turns = 0 # even == red; odd == black
game_over = False
while not game_over:
start_time = time()
if turns % 2 == 0:
action, _ = red.action(state, turns)
else:
action, _ = black.action(state, turns)
end_time = time()
# logger.debug(f"pid = {self.pid}, idx = {idx}, action = {action}, turns = {turns}, time = {(end_time-start_time):.1f}")
if action is None:
logger.debug(f"{turn % 2} (0 = red; 1 = black) has resigned!")
value = -1
break
state = senv.step(state, action)
turns += 1
if turns / 2 >= self.config.play.max_game_length:
game_over = True
value = 0
else:
game_over, value, final_move = senv.done(state)
self.player1.close()
self.player2.close()
if turns % 2 == 1: # black turn
value = -value
if idx % 2 == 1:
value = -value
self.pipes_bt.append(pipe1)
self.pipes_ng.append(pipe2)
return value, turns
def search_action(self, depth, infinite):
no_act = None
_, _, _, check = senv.done(self.state, need_check=True)
logger.debug(f"Check = {check}, state = {self.state}")
if not check and self.state in self.history[:-1]:
no_act = []
for i in range(len(self.history) - 1):
if self.history[i] == self.state:
if senv.will_check_or_catch(self.state,
self.history[i + 1]):
no_act.append(self.history[i + 1])
logger.debug(f"Foul: no act = {no_act}")
action, _ = self.player.action(self.state,
self.turns,
no_act=no_act,
depth=depth,
infinite=infinite,
hist=self.history)
if self.t:
self.t.cancel()
_, value = self.player.debug[self.state]
depth = self.player.done_tasks // 100
self.player.close(wait=False)
self.player = None
self.model.close_pipes()
self.info_best_move(action, value, depth)
def test_done():
import cchess_alphazero.environment.static_env as senv
state = '4s4/9/4e4/p8/2e2R2p/P5E2/8P/9/9/4S1E2'
board = senv.state_to_board(state)
for i in range(9, -1, -1):
print(board[i])
print(senv.done(state))
def fixbug():
from cchess_alphazero.config import Config
from cchess_alphazero.lib.data_helper import get_game_data_filenames, read_game_data_from_file, write_game_data_to_file
import cchess_alphazero.environment.static_env as senv
c = Config('distribute')
files = get_game_data_filenames(c.resource)
cnt = 0
fix = 0
draw_cnt = 0
for filename in files:
try:
data = read_game_data_from_file(filename)
except:
print(f"error: {filename}")
os.remove(filename)
continue
state = data[0]
real_data = [state]
need_fix = True
draw = False
action = None
value = None
is_red_turn = True
for item in data[1:]:
action = item[0]
value = -item[1]
if value == 0:
need_fix = False
draw = True
draw_cnt += 1
break
state = senv.step(state, action)
is_red_turn = not is_red_turn
real_data.append([action, value])
if not draw:
game_over, v, final_move = senv.done(state)
if final_move:
v = -v
is_red_turn = not is_red_turn
if not is_red_turn:
v = -v
if not game_over:
v = 1
# print(game_over, v, final_move, state)
if v == data[1][1]:
need_fix = False
else:
need_fix = True
if need_fix:
write_game_data_to_file(filename, real_data)
# print(filename)
fix += 1
cnt += 1
if cnt % 1000 == 0:
print(cnt, fix, draw_cnt)
print(f"all {cnt}, fix {fix}, draw {draw_cnt}")
def MCTS_search(self, state, history=[], is_root_node=False) -> float:
"""
Monte Carlo Tree Search
"""
while True:
# logger.debug(f"start MCTS, state = {state}, history = {history}")
game_over, v, _ = senv.done(state)
if game_over:
self.executor.submit(self.update_tree, None, v, history)
break
with self.node_lock[state]:
if state not in self.tree:
# Expand and Evaluate
self.tree[state].sum_n = 1
self.tree[state].legal_moves = senv.get_legal_moves(state)
self.tree[state].waiting = True
# logger.debug(f"expand_and_evaluate {state}, sum_n = {self.tree[state].sum_n}, history = {history}")
self.expand_and_evaluate(state, history)
break
if state in history[:-1]: # loop -> loss
# logger.debug(f"loop -> loss, state = {state}, history = {history[:-1]}")
self.executor.submit(self.update_tree, None, 0, history)
break
# Select
node = self.tree[state]
if node.waiting:
node.visit.append(history)
# logger.debug(f"wait for prediction state = {state}")
break
sel_action = self.select_action_q_and_u(state, is_root_node)
virtual_loss = self.config.play.virtual_loss
self.tree[state].sum_n += 1
# logger.debug(f"node = {state}, sum_n = {node.sum_n}")
action_state = self.tree[state].a[sel_action]
action_state.n += virtual_loss
action_state.w -= virtual_loss
action_state.q = action_state.w / action_state.n
# logger.debug(f"apply virtual_loss = {virtual_loss}, as.n = {action_state.n}, w = {action_state.w}, q = {action_state.q}")
if action_state.next is None:
action_state.next = senv.step(state, sel_action)
# logger.debug(f"step action {sel_action}, next = {action_state.next}")
history.append(sel_action)
state = action_state.next
history.append(state)
def MCTS_search(self, state, history=[], is_root_node=False) -> float:
"""
Monte Carlo Tree Search
"""
while True:
# logger.debug(f"start MCTS, state = {state}, history = {history}")
game_over, v, _ = senv.done(state)
if game_over:
self.executor.submit(self.update_tree, None, v, history)
break
with self.node_lock[state]:
if state not in self.tree:
# Expand and Evaluate
self.tree[state].sum_n = 1
self.tree[state].legal_moves = senv.get_legal_moves(state)
self.tree[state].waiting = True
# logger.debug(f"expand_and_evaluate {state}, sum_n = {self.tree[state].sum_n}, history = {history}")
self.expand_and_evaluate(state, history)
break
if state in history[:-1]: # loop -> loss
# logger.debug(f"loop -> loss, state = {state}, history = {history[:-1]}")
self.executor.submit(self.update_tree, None, 0, history)
break
# Select
node = self.tree[state]
if node.waiting:
node.visit.append(history)
# logger.debug(f"wait for prediction state = {state}")
break
sel_action = self.select_action_q_and_u(state, is_root_node)
virtual_loss = self.config.play.virtual_loss
self.tree[state].sum_n += 1
# logger.debug(f"node = {state}, sum_n = {node.sum_n}")
action_state = self.tree[state].a[sel_action]
action_state.n += virtual_loss
action_state.w -= virtual_loss
action_state.q = action_state.w / action_state.n
# logger.debug(f"apply virtual_loss = {virtual_loss}, as.n = {action_state.n}, w = {action_state.w}, q = {action_state.q}")
if action_state.next is None:
action_state.next = senv.step(state, sel_action)
# logger.debug(f"step action {sel_action}, next = {action_state.next}")
history.append(sel_action)
state = action_state.next
history.append(state)
def self_play_buffer(config, pipes_bt, pipes_ng, idx, res_data, hist_base,
hist_ng) -> (tuple, list):
sleep(random())
playouts = randint(8, 12) * 100
config.play.simulation_num_per_move = playouts
logger.info(f"Set playouts = {config.play.simulation_num_per_move}")
pipe1 = pipes_bt.pop() # borrow
pipe2 = pipes_ng.pop()
player1 = CChessPlayer(config,
search_tree=defaultdict(VisitState),
pipes=pipe1,
enable_resign=False,
debugging=False,
use_history=hist_base)
player2 = CChessPlayer(config,
search_tree=defaultdict(VisitState),
pipes=pipe2,
enable_resign=False,
debugging=False,
use_history=hist_ng)
# even: bst = red, ng = black; odd: bst = black, ng = red
if idx % 2 == 0:
red = player1
black = player2
print(f"基准模型执红，待评测模型执黑")
else:
red = player2
black = player1
print(f"待评测模型执红，基准模型执黑")
state = senv.INIT_STATE
history = [state]
# policys = []
value = 0
turns = 0
game_over = False
final_move = None
no_eat_count = 0
check = False
increase_temp = False
no_act = []
while not game_over:
start_time = time()
if turns % 2 == 0:
action, _ = red.action(state,
turns,
no_act=no_act,
increase_temp=increase_temp)
else:
action, _ = black.action(state,
turns,
no_act=no_act,
increase_temp=increase_temp)
end_time = time()
if action is None:
print(f"{turns % 2} (0 = 红; 1 = 黑) 投降了!")
value = -1
break
print(
f"博弈中: 回合{turns / 2 + 1} {'红方走棋' if turns % 2 == 0 else '黑方走棋'}, 着法: {action}, 用时: {(end_time - start_time):.1f}s"
)
# policys.append(policy)
history.append(action)
try:
state, no_eat = senv.new_step(state, action)
except Exception as e:
logger.error(f"{e}, no_act = {no_act}, policy = {policy}")
game_over = True
value = 0
break
turns += 1
if no_eat:
no_eat_count += 1
else:
no_eat_count = 0
history.append(state)
if no_eat_count >= 120 or turns / 2 >= config.play.max_game_length:
game_over = True
value = 0
else:
game_over, value, final_move, check = senv.done(state,
need_check=True)
no_act = []
increase_temp = False
if not game_over:
if not senv.has_attack_chessman(state):
logger.info(f"双方无进攻子力，作和。state = {state}")
game_over = True
value = 0
if not game_over and not check and state in history[:-1]:
free_move = defaultdict(int)
for i in range(len(history) - 1):
if history[i] == state:
if senv.will_check_or_catch(state, history[i + 1]):
no_act.append(history[i + 1])
elif not senv.be_catched(state, history[i + 1]):
increase_temp = True
free_move[state] += 1
if free_move[state] >= 3:
# 作和棋处理
game_over = True
value = 0
logger.info("闲着循环三次，作和棋处理")
break
if final_move:
history.append(final_move)
state = senv.step(state, final_move)
turns += 1
value = -value
history.append(state)
data = []
if idx % 2 == 0:
data = [res_data['base']['digest'], res_data['unchecked']['digest']]
else:
data = [res_data['unchecked']['digest'], res_data['base']['digest']]
player1.close()
player2.close()
del player1, player2
gc.collect()
if turns % 2 == 1: # balck turn
value = -value
v = value
data.append(history[0])
for i in range(turns):
k = i * 2
data.append([history[k + 1], value])
value = -value
pipes_bt.append(pipe1)
pipes_ng.append(pipe2)
return (turns, v, idx), data
def start_game(self, idx, search_tree):
pipes = self.cur_pipes.pop()
if not self.config.play.share_mtcs_info_in_self_play or \
idx % self.config.play.reset_mtcs_info_per_game == 0:
search_tree = defaultdict(VisitState)
if random() > self.config.play.enable_resign_rate:
enable_resign = True
else:
enable_resign = False
self.player = CChessPlayer(self.config,
search_tree=search_tree,
pipes=pipes,
enable_resign=enable_resign,
debugging=False,
use_history=self.use_history)
state = senv.INIT_STATE
history = [state]
# policys = []
value = 0
turns = 0 # even == red; odd == black
game_over = False
final_move = None
no_eat_count = 0
check = False
no_act = []
increase_temp = False
while not game_over:
start_time = time()
action, policy = self.player.action(state,
turns,
no_act,
increase_temp=increase_temp)
end_time = time()
if action is None:
logger.error(f"{turns % 2} (0 = red; 1 = black) has resigned!")
value = -1
break
# if self.config.opts.log_move:
# logger.info(f"Process{self.pid} Playing: {turns % 2}, action: {action}, time: {(end_time - start_time):.1f}s")
# logger.info(f"Process{self.pid} Playing: {turns % 2}, action: {action}, time: {(end_time - start_time):.1f}s")
history.append(action)
# policys.append(policy)
try:
state, no_eat = senv.new_step(state, action)
except Exception as e:
logger.error(f"{e}, no_act = {no_act}, policy = {policy}")
game_over = True
value = 0
break
turns += 1
if no_eat:
no_eat_count += 1
else:
no_eat_count = 0
history.append(state)
if no_eat_count >= 120 or turns / 2 >= self.config.play.max_game_length:
game_over = True
value = 0
else:
game_over, value, final_move, check = senv.done(
state, need_check=True)
if not game_over:
if not senv.has_attack_chessman(state):
logger.error(f"双方无进攻子力，作和。state = {state}")
game_over = True
value = 0
increase_temp = False
no_act = []
if not game_over and not check and state in history[:-1]:
free_move = defaultdict(int)
for i in range(len(history) - 1):
if history[i] == state:
if senv.will_check_or_catch(state, history[i + 1]):
no_act.append(history[i + 1])
elif not senv.be_catched(state, history[i + 1]):
increase_temp = True
free_move[state] += 1
if free_move[state] >= 3:
# 作和棋处理
game_over = True
value = 0
logger.error("闲着循环三次，作和棋处理")
break
if final_move:
# policy = self.build_policy(final_move, False)
history.append(final_move)
# policys.append(policy)
state = senv.step(state, final_move)
turns += 1
value = -value
history.append(state)
self.player.close()
del search_tree
del self.player
gc.collect()
if turns % 2 == 1: # balck turn
value = -value
v = value
if turns < 10:
if random() > 0.9:
store = True
else:
store = False
else:
store = True
if store:
data = [history[0]]
for i in range(turns):
k = i * 2
data.append([history[k + 1], value])
value = -value
self.save_play_data(idx, data)
self.cur_pipes.append(pipes)
self.remove_play_data()
return v, turns, state, store
def start_game(self, idx, search_tree):
pipes = self.cur_pipes.pop()
if not self.config.play.share_mtcs_info_in_self_play or \
idx % self.config.play.reset_mtcs_info_per_game == 0:
search_tree = defaultdict(VisitState)
if random() > self.config.play.enable_resign_rate:
enable_resign = True
else:
enable_resign = False
self.player = CChessPlayer(self.config, search_tree=search_tree, pipes=pipes, enable_resign=enable_resign, debugging=False)
state = senv.INIT_STATE
history = [state]
policys = []
value = 0
turns = 0 # even == red; odd == black
game_over = False
final_move = None
while not game_over:
no_act = None
if state in history[:-1]:
no_act = []
for i in range(len(history) - 1):
if history[i] == state:
no_act.append(history[i + 1])
start_time = time()
action, policy = self.player.action(state, turns, no_act)
end_time = time()
if action is None:
logger.debug(f"{turns % 2} (0 = red; 1 = black) has resigned!")
value = -1
break
# logger.debug(f"Process{self.pid} Playing: {turns % 2}, action: {action}, time: {(end_time - start_time):.1f}s")
# for move, action_state in self.player.search_results.items():
# if action_state[0] >= 20:
# logger.info(f"move: {move}, prob: {action_state[0]}, Q_value: {action_state[1]:.2f}, Prior: {action_state[2]:.3f}")
# self.player.search_results = {}
history.append(action)
policys.append(policy)
state = senv.step(state, action)
turns += 1
history.append(state)
if turns / 2 >= self.config.play.max_game_length:
game_over = True
value = senv.evaluate(state)
else:
game_over, value, final_move = senv.done(state)
if final_move:
policy = self.build_policy(final_move, False)
history.append(final_move)
policys.append(policy)
state = senv.step(state, final_move)
history.append(state)
self.player.close()
if turns % 2 == 1: # balck turn
value = -value
v = value
if v == 0:
if random() > 0.5:
store = True
else:
store = False
else:
store = True
if store:
data = []
for i in range(turns):
k = i * 2
data.append([history[k], policys[i], value])
value = -value
self.save_play_data(idx, data)
self.cur_pipes.append(pipes)
self.remove_play_data()
return v, turns, state, search_tree, store
def start_game(self, idx, search_tree):
pipes = self.cur_pipes.pop()
if not self.config.play.share_mtcs_info_in_self_play or \
idx % self.config.play.reset_mtcs_info_per_game == 0:
search_tree = defaultdict(VisitState)
if random() > self.config.play.enable_resign_rate:
enable_resign = True
else:
enable_resign = False
self.player = CChessPlayer(self.config,
search_tree=search_tree,
pipes=pipes,
enable_resign=enable_resign,
debugging=False)
state = senv.INIT_STATE
history = [state]
value = 0
turns = 0 # even == red; odd == black
game_over = False
is_alpha_red = True if idx % 2 == 0 else False
final_move = None
check = False
while not game_over:
if (is_alpha_red and turns % 2 == 0) or (not is_alpha_red
and turns % 2 == 1):
no_act = None
if not check and state in history[:-1]:
no_act = []
for i in range(len(history) - 1):
if history[i] == state:
no_act.append(history[i + 1])
action, _ = self.player.action(state, turns, no_act)
if action is None:
logger.debug(
f"{turns % 2} (0 = red; 1 = black) has resigned!")
value = -1
break
else:
fen = senv.state_to_fen(state, turns)
action = self.get_ucci_move(fen)
if action is None:
logger.debug(
f"{turns % 2} (0 = red; 1 = black) has resigned!")
value = -1
break
if turns % 2 == 1:
action = flip_move(action)
history.append(action)
state = senv.step(state, action)
turns += 1
history.append(state)
if turns / 2 >= self.config.play.max_game_length:
game_over = True
value = 0
else:
game_over, value, final_move, check = senv.done(
state, need_check=True)
if final_move:
history.append(final_move)
state = senv.step(state, final_move)
history.append(state)
turns += 1
value = -value
self.player.close()
del search_tree
del self.player
gc.collect()
if turns % 2 == 1: # balck turn
value = -value
v = value
if turns <= 10:
if random() > 0.7:
store = True
else:
store = False
else:
store = True
if store:
data = [history[0]]
for i in range(turns):
k = i * 2
data.append([history[k + 1], value])
value = -value
self.save_play_data(idx, data)
self.cur_pipes.append(pipes)
self.remove_play_data()
return v, turns, state, store
def start_game(self, idx):
pipe1 = self.pipes_bt.pop()
pipe2 = self.pipes_ng.pop()
search_tree1 = defaultdict(VisitState)
search_tree2 = defaultdict(VisitState)
self.player1 = CChessPlayer(self.config,
search_tree=search_tree1,
pipes=pipe1,
debugging=False,
enable_resign=True)
self.player2 = CChessPlayer(self.config,
search_tree=search_tree2,
pipes=pipe2,
debugging=False,
enable_resign=True)
# even: bst = red, ng = black; odd: bst = black, ng = red
if idx % 2 == 0:
red = self.player1
black = self.player2
logger.debug(f"best model is red, ng is black")
else:
red = self.player2
black = self.player1
logger.debug(f"best model is black, ng is red")
state = senv.INIT_STATE
value = 0 # best model's value
turns = 0 # even == red; odd == black
game_over = False
while not game_over:
start_time = time()
if turns % 2 == 0:
action, _ = red.action(state, turns)
else:
action, _ = black.action(state, turns)
end_time = time()
# logger.debug(f"pid = {self.pid}, idx = {idx}, action = {action}, turns = {turns}, time = {(end_time-start_time):.1f}")
if action is None:
logger.debug(f"{turn % 2} (0 = red; 1 = black) has resigned!")
value = -1
break
state = senv.step(state, action)
turns += 1
if turns / 2 >= self.config.play.max_game_length:
game_over = True
value = 0
else:
game_over, value, final_move = senv.done(state)
self.player1.close()
self.player2.close()
if turns % 2 == 1: # black turn
value = -value
if idx % 2 == 1: # return player1' value
value = -value
self.pipes_bt.append(pipe1)
self.pipes_ng.append(pipe2)
return value, turns
def self_play_buffer(config, cur) -> (tuple, list):
pipe = cur.pop() # borrow
if random() > config.play.enable_resign_rate:
enable_resign = True
else:
enable_resign = False
player = CChessPlayer(config,
search_tree=defaultdict(VisitState),
pipes=pipe,
enable_resign=enable_resign,
debugging=False)
state = senv.INIT_STATE
history = [state]
policys = []
value = 0
turns = 0
game_over = False
final_move = None
while not game_over:
no_act = None
if state in history[:-1]:
no_act = []
for i in range(len(history) - 1):
if history[i] == state:
no_act.append(history[i + 1])
start_time = time()
action, policy = player.action(state, turns, no_act)
end_time = time()
if action is None:
logger.debug(f"{turns % 2} (0 = red; 1 = black) has resigned!")
value = -1
break
# logger.debug(f"Playing: {turns % 2}, action: {action}, time: {(end_time - start_time):.1f}s")
policys.append(policy)
history.append(action)
state = senv.step(state, action)
turns += 1
history.append(state)
if turns / 2 >= config.play.max_game_length:
game_over = True
value = senv.evaluate(state)
else:
game_over, value, final_move = senv.done(state)
if final_move:
policy = build_policy(final_move, False)
history.append(final_move)
policys.append(policy)
state = senv.step(state, final_move)
history.append(state)
player.close()
if turns % 2 == 1: # balck turn
value = -value
v = value
data = []
for i in range(turns):
k = i * 2
data.append([history[k], policys[i], value])
value = -value
cur.append(pipe)
return (turns, v), data
def start_game(self, idx, search_tree):
pipes = self.cur_pipes.pop()
if not self.config.play.share_mtcs_info_in_self_play or \
idx % self.config.play.reset_mtcs_info_per_game == 0:
search_tree = defaultdict(VisitState)
if random() > self.config.play.enable_resign_rate:
enable_resign = True
else:
enable_resign = False
self.player = CChessPlayer(self.config, search_tree=search_tree, pipes=pipes, enable_resign=enable_resign, debugging=False)
state = senv.INIT_STATE
history = [state]
policys = []
value = 0
turns = 0 # even == red; odd == black
game_over = False
is_alpha_red = True if idx % 2 == 0 else False
final_move = None
while not game_over:
if (is_alpha_red and turns % 2 == 0) or (not is_alpha_red and turns % 2 == 1):
no_act = None
if state in history[:-1]:
no_act = []
for i in range(len(history) - 1):
if history[i] == state:
no_act.append(history[i + 1])
action, policy = self.player.action(state, turns, no_act)
if action is None:
logger.debug(f"{turns % 2} (0 = red; 1 = black) has resigned!")
value = -1
break
else:
fen = senv.state_to_fen(state, turns)
action = self.get_ucci_move(fen)
if action is None:
logger.debug(f"{turns % 2} (0 = red; 1 = black) has resigned!")
value = -1
break
if turns % 2 == 1:
action = flip_move(action)
try:
policy = self.build_policy(action, False)
except Exception as e:
logger.error(f"Build policy error {e}, action = {action}, state = {state}, fen = {fen}")
value = 0
break
history.append(action)
policys.append(policy)
state = senv.step(state, action)
turns += 1
history.append(state)
if turns / 2 >= self.config.play.max_game_length:
game_over = True
value = senv.evaluate(state)
else:
game_over, value, final_move = senv.done(state)
if final_move:
policy = self.build_policy(final_move, False)
history.append(final_move)
policys.append(policy)
state = senv.step(state, final_move)
history.append(state)
self.player.close()
if turns % 2 == 1: # balck turn
value = -value
v = value
if v == 0 or turns <= 10:
if random() > 0.7:
store = True
else:
store = False
else:
store = True
if store:
data = []
for i in range(turns):
k = i * 2
data.append([history[k], policys[i], value])
value = -value
self.save_play_data(idx, data)
self.cur_pipes.append(pipes)
self.remove_play_data()
return v, turns, state, search_tree, store
def start_game(self, idx):
sleep(random())
playouts = randint(8, 12) * 100
self.config.play.simulation_num_per_move = playouts
logger.info(f"Set playouts = {self.config.play.simulation_num_per_move}")
pipe1 = self.pipes_bt.pop()
pipe2 = self.pipes_ng.pop()
search_tree1 = defaultdict(VisitState)
search_tree2 = defaultdict(VisitState)
self.player1 = CChessPlayer(self.config, search_tree=search_tree1, pipes=pipe1,
debugging=False, enable_resign=False, use_history=self.hist_base)
self.player2 = CChessPlayer(self.config, search_tree=search_tree2, pipes=pipe2,
debugging=False, enable_resign=False, use_history=self.hist_ng)
# even: bst = red, ng = black; odd: bst = black, ng = red
if idx % 2 == 0:
red = self.player1
black = self.player2
logger.info(f"进程id = {self.pid} 基准模型执红，待评测模型执黑")
else:
red = self.player2
black = self.player1
logger.info(f"进程id = {self.pid} 待评测模型执红，基准模型执黑")
state = senv.INIT_STATE
history = [state]
value = 0 # best model's value
turns = 0 # even == red; odd == black
game_over = False
no_eat_count = 0
check = False
increase_temp = False
no_act = []
while not game_over:
start_time = time()
if turns % 2 == 0:
action, _ = red.action(state, turns, no_act=no_act, increase_temp=increase_temp)
else:
action, _ = black.action(state, turns, no_act=no_act, increase_temp=increase_temp)
end_time = time()
if self.config.opts.log_move:
logger.debug(f"进程id = {self.pid}, action = {action}, turns = {turns}, time = {(end_time-start_time):.1f}")
if action is None:
logger.debug(f"{turns % 2} (0 = red; 1 = black) has resigned!")
value = -1
break
history.append(action)
state, no_eat = senv.new_step(state, action)
turns += 1
if no_eat:
no_eat_count += 1
else:
no_eat_count = 0
history.append(state)
if no_eat_count >= 120 or turns / 2 >= self.config.play.max_game_length:
game_over = True
value = 0
else:
game_over, value, final_move, check = senv.done(state, need_check=True)
no_act = []
increase_temp = False
if not game_over:
if not senv.has_attack_chessman(state):
logger.info(f"双方无进攻子力，作和。state = {state}")
game_over = True
value = 0
if not game_over and not check and state in history[:-1]:
free_move = defaultdict(int)
for i in range(len(history) - 1):
if history[i] == state:
if senv.will_check_or_catch(state, history[i+1]):
no_act.append(history[i + 1])
elif not senv.be_catched(state, history[i+1]):
increase_temp = True
free_move[state] += 1
if free_move[state] >= 3:
# 作和棋处理
game_over = True
value = 0
logger.info("闲着循环三次，作和棋处理")
break
if final_move:
history.append(final_move)
state = senv.step(state, final_move)
turns += 1
value = - value
history.append(state)
data = []
if idx % 2 == 0:
data = [self.data['base']['digest'], self.data['unchecked']['digest']]
else:
data = [self.data['unchecked']['digest'], self.data['base']['digest']]
self.player1.close()
self.player2.close()
if turns % 2 == 1: # black turn
value = -value
v = value
data.append(history[0])
for i in range(turns):
k = i * 2
data.append([history[k + 1], v])
v = -v
self.pipes_bt.append(pipe1)
self.pipes_ng.append(pipe2)
return value, turns, data
def self_play_buffer(config, cur, use_history=False) -> (tuple, list):
pipe = cur.pop() # borrow
if random() > config.play.enable_resign_rate:
enable_resign = True
else:
enable_resign = False
player = CChessPlayer(config, search_tree=defaultdict(VisitState), pipes=pipe,
enable_resign=enable_resign, debugging=False, use_history=use_history)
state = senv.INIT_STATE
history = [state]
# policys = []
value = 0
turns = 0
game_over = False
final_move = None
no_eat_count = 0
check = False
no_act = None
increase_temp = False
while not game_over:
start_time = time()
action, policy = player.action(state, turns, no_act, increase_temp=increase_temp)
end_time = time()
if action is None:
print(f"{turns % 2} (0 = 红; 1 = 黑) 投降了!")
value = -1
break
print(f"博弈中: 回合{turns / 2 + 1} {'红方走棋' if turns % 2 == 0 else '黑方走棋'}, 着法: {action}, 用时: {(end_time - start_time):.1f}s")
# policys.append(policy)
history.append(action)
try:
state, no_eat = senv.new_step(state, action)
except Exception as e:
logger.error(f"{e}, no_act = {no_act}, policy = {policy}")
game_over = True
value = 0
break
turns += 1
if no_eat:
no_eat_count += 1
else:
no_eat_count = 0
history.append(state)
if no_eat_count >= 120 or turns / 2 >= config.play.max_game_length:
game_over = True
value = 0
else:
game_over, value, final_move, check = senv.done(state, need_check=True)
no_act = []
increase_temp = False
if not game_over:
if not senv.has_attack_chessman(state):
logger.info(f"双方无进攻子力，作和。state = {state}")
game_over = True
value = 0
if not game_over and not check and state in history[:-1]:
free_move = defaultdict(int)
for i in range(len(history) - 1):
if history[i] == state:
if senv.will_check_or_catch(state, history[i+1]):
no_act.append(history[i + 1])
elif not senv.be_catched(state, history[i+1]):
increase_temp = True
free_move[state] += 1
if free_move[state] >= 3:
# 作和棋处理
game_over = True
value = 0
logger.info("闲着循环三次，作和棋处理")
break
if final_move:
# policy = build_policy(final_move, False)
history.append(final_move)
# policys.append(policy)
state = senv.step(state, final_move)
turns += 1
value = -value
history.append(state)
player.close()
del player
gc.collect()
if turns % 2 == 1: # balck turn
value = -value
v = value
data = [history[0]]
for i in range(turns):
k = i * 2
data.append([history[k + 1], value])
value = -value
cur.append(pipe)
return (turns, v), data
def start_game(self, idx, search_tree):
pipes = self.cur_pipes.pop()
if not self.config.play.share_mtcs_info_in_self_play or \
idx % self.config.play.reset_mtcs_info_per_game == 0:
search_tree = defaultdict(VisitState)
if random() > self.config.play.enable_resign_rate:
enable_resign = True
else:
enable_resign = False
self.player = CChessPlayer(self.config,
search_tree=search_tree,
pipes=pipes,
enable_resign=enable_resign,
debugging=False)
state = senv.INIT_STATE
history = [state]
policys = []
value = 0
turns = 0 # even == red; odd == black
game_over = False
final_move = None
while not game_over:
no_act = None
if state in history[:-1]:
no_act = []
for i in range(len(history) - 1):
if history[i] == state:
no_act.append(history[i + 1])
start_time = time()
action, policy = self.player.action(state, turns, no_act)
end_time = time()
if action is None:
logger.debug(f"{turns % 2} (0 = red; 1 = black) has resigned!")
value = -1
break
# logger.debug(f"Process{self.pid} Playing: {turns % 2}, action: {action}, time: {(end_time - start_time):.1f}s")
# for move, action_state in self.player.search_results.items():
# if action_state[0] >= 20:
# logger.info(f"move: {move}, prob: {action_state[0]}, Q_value: {action_state[1]:.2f}, Prior: {action_state[2]:.3f}")
# self.player.search_results = {}
history.append(action)
policys.append(policy)
state = senv.step(state, action)
turns += 1
history.append(state)
if turns / 2 >= self.config.play.max_game_length:
game_over = True
value = senv.evaluate(state)
else:
game_over, value, final_move = senv.done(state)
if final_move:
policy = self.build_policy(final_move, False)
history.append(final_move)
policys.append(policy)
state = senv.step(state, final_move)
history.append(state)
self.player.close()
if turns % 2 == 1: # balck turn
value = -value
v = value
if v == 0:
if random() > 0.5:
store = True
else:
store = False
else:
store = True
if store:
data = []
for i in range(turns):
k = i * 2
data.append([history[k], policys[i], value])
value = -value
self.save_play_data(idx, data)
self.cur_pipes.append(pipes)
self.remove_play_data()
return v, turns, state, search_tree, store
def ai_move(self):
ai_move_first = not self.human_move_first
self.history = [self.env.get_state()]
no_act = None
while not self.env.done:
if ai_move_first == self.env.red_to_move:
labels = ActionLabelsRed
labels_n = len(ActionLabelsRed)
self.ai.search_results = {}
state = self.env.get_state()
logger.info(f"state = {state}")
_, _, _, check = senv.done(state, need_check=True)
if not check and state in self.history[:-1]:
no_act = []
free_move = defaultdict(int)
for i in range(len(self.history) - 1):
if self.history[i] == state:
# 如果走了下一步是将军或捉：禁止走那步
if senv.will_check_or_catch(
state, self.history[i + 1]):
no_act.append(self.history[i + 1])
# 否则当作闲着处理
else:
free_move[state] += 1
if free_move[state] >= 2:
# 作和棋处理
self.env.winner = Winner.draw
self.env.board.winner = Winner.draw
break
if no_act:
logger.debug(f"no_act = {no_act}")
action, policy = self.ai.action(state, self.env.num_halfmoves,
no_act)
if action is None:
logger.info("AI has resigned!")
return
self.history.append(action)
if not self.env.red_to_move:
action = flip_move(action)
key = self.env.get_state()
p, v = self.ai.debug[key]
logger.info(f"check = {check}, NN value = {v:.3f}")
self.nn_value = v
logger.info("MCTS results:")
self.mcts_moves = {}
for move, action_state in self.ai.search_results.items():
move_cn = self.env.board.make_single_record(
int(move[0]), int(move[1]), int(move[2]), int(move[3]))
logger.info(
f"move: {move_cn}-{move}, visit count: {action_state[0]}, Q_value: {action_state[1]:.3f}, Prior: {action_state[2]:.3f}"
)
self.mcts_moves[move_cn] = action_state
x0, y0, x1, y1 = int(action[0]), int(action[1]), int(
action[2]), int(action[3])
chessman_sprite = select_sprite_from_group(
self.chessmans, x0, y0)
sprite_dest = select_sprite_from_group(self.chessmans, x1, y1)
if sprite_dest:
self.chessmans.remove(sprite_dest)
sprite_dest.kill()
chessman_sprite.move(x1, y1, self.chessman_w, self.chessman_h)
self.history.append(self.env.get_state())
def MCTS_search(self,
state,
history=[],
is_root_node=False,
real_hist=None) -> float:
"""
Monte Carlo Tree Search
"""
while True:
# logger.debug(f"start MCTS, state = {state}, history = {history}")
game_over, v, _ = senv.done(state)
if game_over:
v = v * 2
self.executor.submit(self.update_tree, None, v, history)
break
with self.node_lock[state]:
if state not in self.tree:
# Expand and Evaluate
self.tree[state].sum_n = 1
self.tree[state].legal_moves = senv.get_legal_moves(state)
self.tree[state].waiting = True
# logger.debug(f"expand_and_evaluate {state}, sum_n = {self.tree[state].sum_n}, history = {history}")
if is_root_node and real_hist:
self.expand_and_evaluate(state, history, real_hist)
else:
self.expand_and_evaluate(state, history)
break
if state in history[:-1]: # loop
for i in range(len(history) - 1):
if history[i] == state:
if senv.will_check_or_catch(state, history[i + 1]):
self.executor.submit(self.update_tree, None,
-1, history)
elif senv.be_catched(state, history[i + 1]):
self.executor.submit(self.update_tree, None, 1,
history)
else:
# logger.debug(f"loop -> loss, state = {state}, history = {history[:-1]}")
self.executor.submit(self.update_tree, None, 0,
history)
break
break
# Select
node = self.tree[state]
if node.waiting:
node.visit.append(history)
# logger.debug(f"wait for prediction state = {state}")
break
sel_action = self.select_action_q_and_u(state, is_root_node)
virtual_loss = self.config.play.virtual_loss
self.tree[state].sum_n += 1
# logger.debug(f"node = {state}, sum_n = {node.sum_n}")
action_state = self.tree[state].a[sel_action]
action_state.n += virtual_loss
action_state.w -= virtual_loss
action_state.q = action_state.w / action_state.n
# logger.debug(f"apply virtual_loss = {virtual_loss}, as.n = {action_state.n}, w = {action_state.w}, q = {action_state.q}")
# if action_state.next is None:
history.append(sel_action)
state = senv.step(state, sel_action)
history.append(state)
