def replay():
print('dqn_setting')
sess = tf.Session()
game = Sim(SCREEN_WIDTH, SCREEN_HEIGHT, show_game=True)
brain = DQN(sess, VIEW_WIDTH, VIEW_HEIGHT, NUM_ACTION)
saver = tf.train.Saver()
ckpt = tf.train.get_checkpoint_state('model')
saver.restore(sess, ckpt.model_checkpoint_path)
# start game
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()
print('action_choice : ' + str(action))
# get data
state, reward, terminal = game.Update(action)
total_reward += reward
brain.remember(state, action, reward, terminal)
# show the play
time.sleep(10)
print('Number of game: %d Score: %d' % (episode + 1, total_reward))
def train():
print('dqn_setting')
sess = tf.Session()
game = Sim(SCREEN_WIDTH, SCREEN_HEIGHT)
brain = DQN(sess, VIEW_WIDTH, VIEW_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()
# initialize target network
brain.update_target_network()
# decide action decision time using dqn after this time
epsilon = 1.0
# number of frames
time_step = 0
total_reward_list = []
# Starting
for episode in range(MAX_EPISODE):
terminal = False
total_reward = 0
# gmae reset and get state (=poisition data)
state = game.Reset()
brain.init_state(state) # set state of dqn
while not terminal:
# choice random action when epsilon < random var, else ( :epilon > random var), choice with dqn
# Later time, unless use random action
if np.random.rand() < epsilon:
action = game.get_action()
else:
action = brain.get_action()
# epsion decrease
if episode > OBSERVE:
epsilon -= 1 / 1000
# game updates, get data (state / reward / is_gameover)
state, reward, terminal = game.Update(action)
total_reward += reward
# brain save this state
brain.remember(state, action, reward, terminal)
# After little time, In interval, do training
if time_step > OBSERVE and time_step % TRAIN_INTERVAL == 0:
brain.train()
# In interval, do training
if time_step % TARGET_UPDATE_INTERVAL == 0:
brain.update_target_network()
time_step += 1
print('Number of game: %d Score: %d' % (episode + 1, total_reward))
f = open('ac.txt', 'rw')
f.write('Number of game: %d Score: %d ::\n' %
(episode + 1, total_reward))
f.close()
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)
