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):
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):
# 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_array(), 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
