def train():
print('뇌세포 꺠우는 중..')
sess = tf.Session()
game = Game(screenWidth, screenHeight, show_game=False)
brain = DQN(sess, screenWidth, screenHeight, numAction)
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)
summaryMerged = tf.summary.merge_all()
brain.updateTargetNetwork()
timeStep = 0
totalRewardList = []
for episode in range(maxEpisode):
terminal = False
totalReward = 0
epsilon = 1.0
state = game.reset()
brain.initState(state)
while not terminal:
if np.random.rand() < epsilon:
action = random.randrange(numAction)
else:
action = brain.getAction()
if episode > observe:
epsilon -= 1 / 1000
state, reward, terminal = game.step(action)
totalReward += reward
brain.remember(state, action, reward, terminal)
if timeStep > observe and timeStep % trainInterval == 0:
brain.train()
if timeStep % targetUpdateInterval == 0:
brain.updateTargetNetwork()
timeStep += 1
totalRewardList.append(totalReward)
if episode % 10 == 0:
summary = sess.run(summaryMerged,
feed_dict={rewards: totalRewardList})
writer.add_summary(summary, timeStep)
if episode % 100 == 99:
print("게임횟수 : {0}, 점수 : {1:.4f}".format(episode + 1, totalReward))
saver.save(sess, './model/dqn.ckpt', global_step=timeStep)
def replay():
print('뇌세포 깨우는 중..')
sess = tf.Session()
game = Game(screenWidth, screenHeight, show_game=True)
brain = DQN(sess, screenWidth, screenHeight, numAction)
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state('model')
saver.restore(sess, ckpt.model_checkpoint_path)
for episode in range(maxEpisode):
terminal = False
totalReward = 0
state = game.reset()
brain.initState(state)
while not terminal:
action = brain.getAction()
state, reward, terminal = game.step(action)
totalReward += reward
time.sleep(0.3)
print("게임횟수 : {0}, 점수 : {1}".format(episode + 1, totalReward))
