class DqnHalfPongSyr(PyGamePlayer):
def __init__(self, playback_mode, mod=None, net_name='pong_syr'):
self._playback_mode = playback_mode
self._last_reward = 0
super(DqnHalfPongSyr, self).__init__(force_game_fps=8,
run_real_time=playback_mode)
self._last_state = None
self._last_action = np.zeros(DIM_ACTION)
self._last_action[1] = 1
self.sess = tf.Session()
self.agent = DDQNAgent(self.sess,
DIM_STATE,
DIM_ACTION,
LR,
TAU,
net_name=net_name)
self.sess.run(tf.global_variables_initializer())
self.agent.update_target_paras()
self.saver = tf.train.Saver()
self.replay_buffer = ReplayBuffer(BUFFER_SIZE)
self.explorer = Explorer(EPS_BEGIN, EPS_END, EPS_STEPS, playback_mode)
self.summary = Summary(self.sess, DIR_SUM)
self.summary.add_variable(tf.Variable(0.), 'reward')
self.summary.add_variable(tf.Variable(0.), 'loss')
self.summary.build()
self.summary.write_variables(FLAGS)
self._steps = 0
self._sum_reward = [0]
self._dif_reward = deque(maxlen=EP_STEPS)
if mod and os.path.exists(FLAGS.dir_mod.format(mod)):
checkpoint = tf.train.get_checkpoint_state(
FLAGS.dir_mod.format(mod))
self.saver.restore(self.sess,
save_path=checkpoint.model_checkpoint_path)
print("Loaded checkpoints {0}".format(
checkpoint.model_checkpoint_path))
def get_keys_pressed(self, screen_array, feedback, terminal):
_, screen_binary = cv2.threshold(
cv2.cvtColor(screen_array, cv2.COLOR_BGR2GRAY), 1, 255,
cv2.THRESH_BINARY)
if self._last_state is None:
self._last_state = np.stack(tuple(screen_binary
for _ in range(STATE_FRAMES)),
axis=2)
return DqnHalfPongSyr._key_presses_from_action(self._last_action)
screen_binary = np.reshape(screen_binary,
(SCREEN_WIDTH, SCREEN_HEIGHT, 1))
current_state = np.append(self._last_state[:, :, 1:],
screen_binary,
axis=2)
if not self._playback_mode:
self.replay_buffer.add(self._last_state, self._last_action,
feedback, current_state, terminal)
if len(self.replay_buffer) > OBV_STEPS:
loss = self._train()
self._sum_reward.append(feedback)
if feedback != 0.0:
self._dif_reward.append(feedback)
if not self._steps % EP_STEPS:
print(
'| Step: %i' % self._steps,
'| Epoch: %i' % (self._steps / EP_STEPS),
'| Sum_Reward: %i' % sum(self._sum_reward),
'| Dif_Reward: %.4f' %
(sum(self._dif_reward) / len(self._dif_reward)))
if not self._steps % (EP_STEPS * 10):
self.summary.run(feed_dict={
'loss': loss,
'reward': sum(self._sum_reward)
})
self._sum_reward = [0]
self._last_state = current_state
self._last_action = self._get_action()
return DqnHalfPongSyr._key_presses_from_action(self._last_action)
def _get_action(self):
target_q = self.agent.predict([self._last_state])[0]
return self.explorer.get_action(target_q)
def _train(self):
self._steps += 1
batch_state, batch_action, batch_reward, batch_state_next, batch_done = \
self.replay_buffer.sample_batch(MINI_BATCH)
q_value = self.agent.predict(batch_state_next)
max_q_value_index = np.argmax(q_value, axis=1)
target_q_value = self.agent.predict_target(batch_state_next)
double_q = target_q_value[range(len(target_q_value)),
max_q_value_index]
batch_y = []
for r, q, d in zip(batch_reward, double_q, batch_done):
if d:
batch_y.append(r)
else:
batch_y.append(r + GAMMA * q)
opt, loss = self.agent.train(batch_state, batch_action, batch_y)
self.agent.update_target_paras()
if not self._steps % CKP_STEP:
self.saver.save(self.sess,
DIR_MOD + '/net',
global_step=self._steps)
print('Mod saved!')
return loss
def get_feedback(self):
from Env.games.half_pong import score
# get the difference in score between this and the last run
score_change = (score - self._last_reward)
self._last_reward = score
return float(score_change), score_change == -1
@staticmethod
def _key_presses_from_action(action_set):
if action_set[0] == 1:
return [K_DOWN]
elif action_set[1] == 1:
return []
elif action_set[2] == 1:
return [K_UP]
raise Exception("Unexpected action")
def start(self):
super(DqnHalfPongSyr, self).start()
from Env.games.half_pong import run
run(screen_width=SCREEN_WIDTH, screen_height=SCREEN_HEIGHT)
class DdqnPong():
def __init__(self, playback_mode, env, render=True, mod=None):
self._playback_mode = playback_mode
self._env = env
self._render = render
self._sess = tf.Session()
self._agent = DDQNAgent(self._sess, DIM_STATE, DIM_ACTION, LR, TAU, net_name='flat')
self._sess.run(tf.global_variables_initializer())
self._agent.update_target_paras()
self._saver = tf.train.Saver()
self._replay_buffer = ReplayBuffer(BUFFER_SIZE)
self._explorer = Explorer(EPS_BEGIN, EPS_END, EPS_STEPS, playback_mode)
self.summary = Summary(self._sess, DIR_SUM)
self.summary.add_variable(tf.Variable(0.), 'reward')
self.summary.add_variable(tf.Variable(0.), 'loss')
self.summary.add_variable(tf.Variable(0.), 'maxq')
self.summary.build()
self.summary.write_variables(FLAGS)
self._steps = 0
if mod and os.path.exists(FLAGS.dir_mod.format(mod)):
checkpoint = tf.train.get_checkpoint_state(FLAGS.dir_mod.format(mod))
self._saver.restore(self._sess, save_path=checkpoint.model_checkpoint_path)
print("Loaded checkpoints {0}".format(checkpoint.model_checkpoint_path))
def start(self):
for ep in range(MAX_EP):
sum_reward = 0
last_state = []
last_img = self._env.reset()
last_img = (pre_process_image(last_img, SCREEN_WIDTH, SCREEN_HEIGHT))
for _ in range(STATE_FRAMES):
last_state.append(last_img)
last_state = np.dstack(last_state)
for step in range(EP_STEPS):
if self._render:
env.render()
q_value = self._agent.predict([last_state])[0]
last_max_qvalue = np.max(q_value)
act_1_hot = self._explorer.get_action(q_value)
act_index = np.argmax(act_1_hot)
# ['NOOP-XX', 'FIRE->NOOP->0', 'RIGHT->UP->1', 'LEFT->DOWN->2', 'RIGHTFIRE-XX', 'LEFTFIRE-XX']
observation, reward, done, info = env.step(act_index+1)
if reward == 0:
reward += 0.1
state = pre_process_image(observation, SCREEN_WIDTH, SCREEN_HEIGHT)
state = np.reshape(state, (SCREEN_WIDTH, SCREEN_HEIGHT, 1))
state = np.append(state, last_state[:, :, :3], axis=2)
self._replay_buffer.add(last_state, act_1_hot, reward, state, done)
loss = None
if not self._playback_mode and len(self._replay_buffer) > OBV_STEPS:
loss = self._train()
last_state = state
sum_reward += reward
self._steps += 1
if done or step == EP_STEPS - 1:
print('| Step: %i' % self._steps,
'| Episode: %i' % ep,
'| Epoch: %i' % step,
'| qvalue: %.5f' % last_max_qvalue,
'| Sum_Reward: %i' % sum_reward)
if loss != None:
self.summary.run(feed_dict={
'loss': loss,
'reward': sum_reward,
'maxq': last_max_qvalue})
break
def _train(self):
batch_state, batch_action, batch_reward, batch_state_next, batch_done = \
self._replay_buffer.sample_batch(MINI_BATCH)
q_value = self._agent.predict(batch_state_next)
max_q_value_index = np.argmax(q_value, axis=1)
target_q_value = self._agent.predict_target(batch_state_next)
double_q = target_q_value[range(len(target_q_value)), max_q_value_index]
batch_y = []
for r, q, d in zip(batch_reward, double_q, batch_done):
if d:
batch_y.append(r)
else:
batch_y.append(r + GAMMA * q)
opt, loss = self._agent.train(batch_state, batch_action, batch_y)
self._agent.update_target_paras()
if not self._steps % CKP_STEP:
self._saver.save(self._sess, DIR_MOD + '/net', global_step=self._steps)
print('Mod saved!')
return loss
class DdqnBirdSyr():
def __init__(self, playback_mode, mod=None):
self._playback_mode = playback_mode
env = FlappyBird(pipe_gap=200)
self._ple = PLE(env, fps=30, display_screen=DISPLAY)
self._ple.init()
self._sess = tf.Session()
self._agent = DDQNAgent(self._sess, DIM_STATE, DIM_ACTION, LR, TAU, net_name='cnn_bird')
self._sess.run(tf.global_variables_initializer())
self._agent.update_target_paras()
self._saver = tf.train.Saver()
self._replay_buffer = ReplayBuffer(BUFFER_SIZE)
self._explorer = Explorer(EPS_BEGIN, EPS_END, EPS_STEPS, playback_mode)
self.summary = Summary(self._sess, DIR_SUM)
self.summary.add_variable(tf.Variable(0.), 'reward')
self.summary.add_variable(tf.Variable(0.), 'loss')
self.summary.add_variable(tf.Variable(0.), 'maxq')
self.summary.build()
self.summary.write_variables(FLAGS)
self._steps = 0
if mod and os.path.exists(FLAGS.dir_mod.format(mod)):
checkpoint = tf.train.get_checkpoint_state(FLAGS.dir_mod.format(mod))
self._saver.restore(self._sess, save_path=checkpoint.model_checkpoint_path)
print("Loaded checkpoints {0}".format(checkpoint.model_checkpoint_path))
def start(self):
for ep in range(MAX_EP):
sum_reward = 0
last_state = []
for _ in range(STATE_FRAMES):
last_state.append(self._ple.getScreenGrayscale())
last_state = np.dstack(last_state)
last_max_qvalue = 0
for step in range(EP_STEPS):
time.sleep(0.01)
if not step % STATE_FRAMES:
q_value = self._agent.predict([last_state])[0]
last_max_qvalue = np.max(q_value)
act_1_hot = self._explorer.get_action(q_value)
act_index = np.argmax(act_1_hot)
else:
# do nothing
act_index = 1
act_1_hot = np.zeros(DIM_ACTION)
act_1_hot[act_index] = 1
reward = self._ple.act(self._ple.getActionSet()[act_index])
if reward == 0:
reward = 0.1
elif reward == -5:
reward = -1
state = np.reshape(self._ple.getScreenGrayscale(), (SCREEN_WIDTH, SCREEN_HEIGHT, 1))
state = np.append(state, last_state[:, :, :3], axis=2)
done = False
if self._ple.game_over():
done = True
self._replay_buffer.add(last_state, act_1_hot, reward, state, done)
loss = None
if not self._playback_mode and len(self._replay_buffer) > OBV_STEPS:
loss = self._train()
last_state = state
sum_reward += reward
self._steps += 1
if done or step == EP_STEPS - 1:
print('| Step: %i' % self._steps,
'| Episode: %i' % ep,
'| Epoch: %i' % step,
'| qvalue: %.5f' % last_max_qvalue,
'| Sum_Reward: %i' % sum_reward)
if loss != None:
self.summary.run(feed_dict={
'loss': loss,
'reward': sum_reward,
'maxq': last_max_qvalue})
self._ple.reset_game()
break
def _train(self):
batch_state, batch_action, batch_reward, batch_state_next, batch_done = \
self._replay_buffer.sample_batch(MINI_BATCH)
q_value = self._agent.predict(batch_state_next)
max_q_value_index = np.argmax(q_value, axis=1)
target_q_value = self._agent.predict_target(batch_state_next)
double_q = target_q_value[range(len(target_q_value)), max_q_value_index]
batch_y = []
for r, q, d in zip(batch_reward, double_q, batch_done):
if d:
batch_y.append(r)
else:
batch_y.append(r + GAMMA * q)
opt, loss = self._agent.train(batch_state, batch_action, batch_y)
self._agent.update_target_paras()
if not self._steps % CKP_STEP:
self._saver.save(self._sess, DIR_MOD + '/net', global_step=self._steps)
print('Mod saved!')
return loss