def play(model):
# 학습 데이터
history = []
# 상태 생성
state = State()
while True:
# 게임 종료 시
if state.is_done():
break
# 합법적인 수의 확률 분포 얻기
scores = pv_mcts_scores(model, state, SP_TEMPERATURE)
# 학습 데이터에 상태와 정책 추가
policies = [0] * DN_OUTPUT_SIZE
for action, policy in zip(state.legal_actions(), scores):
policies[action] = policy
history.append([[state.pieces, state.enemy_pieces], policies, None])
# 행동 얻기
action = np.random.choice(state.legal_actions(), p=scores)
# 다음 상태 얻기
state = state.next(action)
# 학습 데이터에 가치 추가
value = first_player_value(state)
for i in range(len(history)):
history[i][2] = value
value = -value
return history
def play(model):
history = []
state = State()
while True:
if state.is_done():
break
scores = pv_mcts_scores(model, state, SP_TEMPERATURE)
policies = [0] * DN_OUTPUT_SIZE
for action, policy in zip(state.legal_actions(), scores):
policies[action] = policy
history.append([state.pieces_array(), policies, None])
action = np.random.choice(state.legal_actions(), p=scores)
state = state.next(action)
value = first_player_value(state)
for i in range(len(history)):
history[i][2] = value
value = -value
return history
def play(model):
states = []
ys = [[], None]
state = State()
while True:
if state.end:
break
scores = pv_mcts_scores(model, MCTS_EVALUATE_COUNT, state)
policies = [0] * 9
for action, policy in zip(state.legal_actions, boltzman(scores, 1.0)):
policies[action] = policy
states.append(state)
ys[0].append(policies)
state = state.next(
np.random.choice(state.legal_actions,
p=boltzman(scores, TEMPERATURE)))
value = first_player_value(state)
ys[1] = tuple(take(len(ys[0]), cycle((value, -value))))
return states, ys
def play(model):
# 学習データ
history = []
# 状態の生成
state = State()
while True:
# ゲーム終了時
if state.is_done():
break
# 合法手の確率分布の取得
scores = pv_mcts_scores(model, state, SP_TEMPERATURE)
# 学習データに状態と方策を追加
policies = [0] * DN_OUTPUT_SIZE
for action, policy in zip(state.legal_actions(), scores):
policies[action] = policy
history.append([state.pieces_array(), policies, None])
# 行動の取得
action = np.random.choice(state.legal_actions(), p=scores)
# 次の状態の取得
state = state.next(action)
# 学習データに価値を追加
value = first_player_value(state)
for i in range(len(history)):
history[i][2] = value
value = -value
return history
def play(model):
# 学習データ
history = []
# 状態の生成
state = State()
while True:
# ゲーム終了時
if state.is_done():
break
# 合法手の確率分布の取得
scores, values = pv_mcts_scores(model, state, SP_TEMPERATURE)
# 学習データに状態と方策を追加
policies = [0] * DN_OUTPUT_SIZE
for action, policy in zip(state.legal_actions(), scores):
policies[action] = policy
# 行動の取得
action = np.random.choice(state.legal_actions(), p=scores)
# state, policy, value, 探索結果, 選ばれた手、それから先の局面
history.append([[state.pieces, state.enemy_pieces], policies, None,
values, action, None])
# 次の状態の取得
state = state.next(action)
# 学習データに価値を追加
value = first_player_value(state)
for i in range(len(history)):
history[i][2] = value
value = -value
# 最後の局面情報を取っておく
last_state = history[-1][0]
last_policy = [0] * DN_OUTPUT_SIZE
v0 = history[0][2]
v1 = history[1][2]
for i in range(len(history)):
rp = []
for inc in range(3):
index = i + inc
if index < len(history):
rp.append(history[i + inc])
else:
v = v0 if ((i + inc) % 2) == 0 else v1
a = randint(9)
rp.append([last_state, last_policy, v, v, a, None])
history[i][5] = rp
return history
def play(model, using_saved_state=False, saving_ontheway_state=False):
'''
1ゲームの実行
'''
# 学習データ
history = []
# 状態の生成
if using_saved_state:
state = load_state()
if not state:
state = State()
else:
state = State()
starttime = time.time()
print('')
while True:
# ゲーム終了時
if state.is_done():
endtime = time.time()
print("first player is ", "lose" if state.is_lose() else "win")
print("first player num:", state.piece_count(state.pieces))
print('elapsed time', endtime - starttime)
print(state)
break
# 合法手の確率分布の取得
scores = pv_mcts_scores(model, state, SP_TEMPERATURE)
# 学習データに状態と方策を追加
policies = [0] * DN_OUTPUT_SIZE
for action, policy in zip(state.legal_actions(), scores):
policies[action] = policy
history.append([[state.pieces, state.enemy_pieces], policies, None])
# 行動の取得
if len(history) % 10 == 0:
print("state len: ", len(history))
print(state)
if saving_ontheway_state and len(history) == 25:
save_state(state)
action = np.random.choice(state.legal_actions(), p=scores)
# 次の状態の取得
state = state.next(action)
# 学習データに価値を追加
value = first_player_value(state)
for i in range(len(history)):
history[i][2] = value
value = -value
return history
def play(models):
state = State()
for model in cycle(models):
if state.end:
break
state = state.next(
np.random.choice(state.legal_actions,
p=boltzman(
pv_mcts_scores(model, MCTS_EVALUATE_COUNT,
state), TEMPERATURE)))
return first_player_point(state)
def play(model):
history = []
state = State()
while True:
if state.is_done():
break
scores = pv_mcts_scores(model, state, SP_TEMPERATURE)
with open('action_list.txt', 'rb') as f:
action_list = pickle.load(f)
# print('action_list:', len(action_list))
policies = np.zeros(len(action_list))
# for action_num, policy in zip(state.legal_actions(), scores):
# policies[action_num] = policy
# print('size check', len(policies), len(scores))
legal_actions = state.legal_actions()
for i in range(len(legal_actions)):
policies[legal_actions[i]] = scores[i]
# print(policies)
# print('policies:', policies)
history.append([[state.pieces, state.enemy_pieces], policies, None])
# action_list_num = np.arange(len(action_list))
# action_num = np.random.choice(action_list_num, p=scores)
action_num = np.random.choice(legal_actions, p=scores)
# print(action_num)
state.next(action_num)
value = first_player_value(state)
for i in range(len(history)):
history[i][2] = value
value = -value
return history
def play(model):
# 학습 데이터
history = []
# 상태 생성
state = State()
while True:
# 게임 종료 시
if state.is_done():
break
# 합법적인 수의 확률 분포 얻기
# (모델, 게임 상태, 온도파라미터:변동성을주기위해사용하는변수)
# 각 노드의 점수가 계산
scores = pv_mcts_scores(model, state, SP_TEMPERATURE)
# 학습 데이터에 상태와 정책 추가
policies = [0] * DN_OUTPUT_SIZE # 행동수 :7
# 돌을 놓을수 있는 후보지, 점수를 넣어서
for action, policy in zip(state.legal_actions(), scores):
# 행동과 정책을 세팅
# 어떤 열에 정책 세팅
policies[action] = policy
# 내역을 기록 ( [내돌상태, 적돌상태], 정책, None(점수))
history.append([[state.pieces, state.enemy_pieces], policies, None])
# 행동 얻기
action = np.random.choice(state.legal_actions(), p=scores)
# 다음 상태 얻기
state = state.next(action)
# 학습 데이터에 가치 추가
value = first_player_value(state)
for i in range(len(history)):
history[i][2] = value
value = -value
return history
def play(model, p_fail_count):
history = []
state = go.Position()
while True:
if state.is_game_over():
break
scores = pv_mcts_scores(model, state, SP_TEMPERATURE, p_fail_count)
policies = [0] * DN_OUTPUT_SIZE
for action, policy in zip(get_legal_actions(state.all_legal_moves()), scores):
policies[action] = policy
x = features.extract_features(state, features.AGZ_FEATURES)
history.append([x, policies, None])
pv_mcts_coord = None
action = np.random.choice(get_legal_actions(state.all_legal_moves()), p=scores)
if action == (go.N * go.N):
pv_mcts_coord = None
else:
coord_row = action // go.N
coord_column = action % go.N
pv_mcts_coord = (coord_row, coord_column)
state = state.play_move(pv_mcts_coord)
value = state.result()
for i in range(len(history)):
history[i][2] = value
value = -value
return history
