import tensorflow as tf

	 ------------------- e
	 sigma*(2*pi)^(1/2)
'''
def gaussian_pdf(mean, loc_std, sample):
	Z = 1.0 / (loc_std * tf.sqrt(2.0 * np.pi))
	a = - tf.square(sample - mean) / (2.0 * tf.square(loc_std))
	return Z * tf.exp(a)

class ACNet:
	def __init__(self, scope, GRID_SIZE, a_size, trainer,TRAINING, GLOBAL_NET_SCOPE):
		with tf.variable_scope(str(scope)+'/qvalues'):
			#The input size may require more work to fit the interface.
			self.inputs = tf.placeholder(shape=[None,GRID_SIZE,GRID_SIZE, num_channels], dtype=tf.float32)		# input state
			# self.goal_pos = tf.placeholder(shape=[None,2],dtype=tf.float32)
			self.prev_loc = tf.placeholder(shape=[None,2], dtype=tf.float32)

#            self.policy, self.next_loc, self.value, self.state_out, self.state_in, self.state_init, self.valids, self.blocking, self.mypos, self.goalpos, self.next_loc_mean = self._build_net(self.inputs, self.inputs_primal, self.prev_loc, RNN_SIZE, TRAINING,a_size)

			'''
			CHANGES
			- removed target_blocking, blocking layers, blocking_loss
			- removed imitation gradients and losss
			- removed valid_loss
			- removed train_valid
