global_step = 0
scores, episodes = [], []
for e in range(EPISODES):
done = False
score = 0
state = env.reset()
state = np.reshape(state, [1, player1.state_size])
while not done:
# env 초기화
global_step += 1
# 홀수 턴(qlearning player) - Black
if env.get_turn() % 2 == 1:
# 현재 상태에 대한 행동 선택
action = player1.get_action(state)
# 선택한 행동으로 환경에서 한 타임스텝 진행 후 샘플 수집
next_state, reward, done = env.step(BLACK, action)
'''
print("Action : ", action)
print("Reward : ", reward)
print("Next State : ", next_state)
print()
'''
next_state = np.reshape(next_state, [1, player1.state_size])
next_action = player1.get_action(next_state)
# 샘플로 모델 학습
player1.train_model(state, action, reward, next_state,
next_action, done)
next_state, reward, done = env.step(BLACK, action)
next_state = np.reshape(next_state, [1, player.state_size])
if PRINT_FLAG:
print("Action : {0} ==> {1}, {2}".format(
action, int(action / 10), action % 10))
print("Reward : ", reward)
print("Next State : ", next_state)
print()
player.append_sample(state, action, reward)
score += reward
state = copy.deepcopy(next_state)
if PRINT_FLAG:
# board 출력
print("Episode : {0}, Turn : {1}, PLAYER1".format(
e, env.get_turn()))
env.draw_board()
print()
time.sleep(1)
# White
else:
# 현재 상태 획득
state = env.get_state()
state = np.reshape(state, [1, player.state_size])
# 현재 상태에 대한 행동 선택
action = player.get_action(state)
# 선택한 행동으로 환경에서 한 타임스텝 진행 후 샘플 수집
next_state, reward, done = env.step(BLACK, action)
next_state = np.reshape(next_state, [1, player.state_size])
if PRINT_FLAG:
print("Action : {0} ==> {1}, {2}".format(
