def step_after(self,reward, action, eps, q_now, obs_array):

		'''
		Step after
		'''

		self.reward = reward
		# Learning Phase
		if self.policyFrozen is False:  # Learning ON/OFF
			self.func.stock_experience(self.time, self.last_state, self.lastAction, reward, self.state, False)
			self.func.experience_replay(self.time)

		# Target model update
		if self.time < self.func.initial_exploration and np.mod(self.time, self.func.target_model_update_freq) == 0:
			print 'Model Updated'
			self.func.target_model_update()

		# Simple text based visualization
		if Deel.gpu >= 0:
			q_max = np.max(q_now.get())
		else:
			q_max = np.max(q_now)
		print 'Step %d/ACT %d/R %.1f/EPS %.6f/Q_max %3f' % (
			self.time, self.func.action_to_index(action), reward, eps, q_max)

		# Updates for next step
		self.last_observation = obs_array

		if self.policyFrozen is False:
			self.lastAction = copy.deepcopy(action)
			self.last_state = self.state.copy()
			self.time += 1

			#if self.time % 1000 == 0:
			#	pickle.dump(self.func.model, open("cash/qnet_%05d.pkl"%self.time, 'wb'))
dataset/ETHPy150Open uei/deel/deel/network/dqn.py/DQN.step_after/swapped operands of `<`