import tensorflow as tf

			# Loss Functions
			self.value_loss     = 0.5*tf.reduce_sum(tf.square(self.target_v - tf.reshape(self.value, shape=[-1])))

			# H(x) = Sum[p(x)*log(p(x))]
			self.entropy        = - 0.01 * tf.reduce_sum(self.policy * tf.log(tf.clip_by_value(self.policy,1e-10,1.0)))

			self.policy_loss  = - 0.2 * tf.reduce_sum( tf.log(tf.clip_by_value(self.policy[:,0],1e-15,1.0)) * self.advantages + tf.log(tf.clip_by_value(self.policy[:,1],1e-15,1.0)) * self.advantages)

			#For Normal RL Part
			self.loss           = self.value_loss + self.policy_loss - self.entropy		# removed self.blocking_loss, valid_loss, discrete_policy _loss         #+ 0.5*self.mypos_loss + 0.5*self.goalpos_loss

			#For Imitation Learning Part
