def test():
print('뇌세포 깨우는 중..')
sess = tf.Session()
game = Game(SCREEN_WIDTH, SCREEN_HEIGHT, show_game=True)
brain = DQN(sess, SCREEN_WIDTH, SCREEN_HEIGHT, NUM_ACTION)
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state('model')
saver.restore(sess, ckpt.model_checkpoint_path)
for episode in range(MAX_EPISODE):
terminal = False
total_reward = 0
state = game.reset()
brain.init_state(state)
while not terminal:
action = brain.get_action()
state, reward, terminal = game.step(action)
total_reward += reward
brain.remember(state, action, reward, terminal)
time.sleep(0.3)
print('게임횟수: %d 점수: %d' % (episode + 1, total_reward))
def train():
print('뇌세포 깨우는 중..')
sess = tf.Session()
game = Game(SCREEN_WIDTH, SCREEN_HEIGHT, show_game=False)
brain = DQN(sess, SCREEN_WIDTH, SCREEN_HEIGHT, NUM_ACTION)
rewards = tf.placeholder(tf.float32, [None])
tf.summary.scalar('avg.reward/ep.', tf.reduce_mean(rewards))
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
writer = tf.summary.FileWriter('logs', sess.graph)
summary_merged = tf.summary.merge_all()
brain.update_target_network()
epsilon = 1.0
time_step = 0
total_reward_list = []
# 게임을 시작합니다.
for episode in range(MAX_EPISODE):
terminal = False
total_reward = 0
state = game.reset()
brain.init_state(state)
while not terminal:
if np.random.rand() < epsilon:
action = random.randrange(NUM_ACTION)
else:
action = brain.get_action()
if episode > OBSERVE:
epsilon -= 1 / 1000
state, reward, terminal = game.step(action)
total_reward += reward
brain.remember(state, action, reward, terminal)
if time_step > OBSERVE and time_step % TRAIN_INTERVAL == 0:
brain.train()
if time_step % TARGET_UPDATE_INTERVAL == 0:
brain.update_target_network()
time_step += 1
print('게임횟수: %d 점수: %d' % (episode + 1, total_reward))
total_reward_list.append(total_reward)
if episode % 10 == 0:
summary = sess.run(summary_merged, feed_dict={rewards: total_reward_list})
writer.add_summary(summary, time_step)
total_reward_list = []
if episode % 100 == 0:
saver.save(sess, 'model/dqn.ckpt', global_step=time_step)
