IIDQN: An Incentive Improved DQN Algorithm in EBSN Recommender System

Algorithm 1

Incentive improvement algorithm based on Q networks in EBSN.

	IIDQN algorithm
	Inputs: state space , action space , discount rate , learning rate , parameter update interval .
(1)	Randomly initialize the parameters of the Q-networks and randomly initialize the parameters of the target Q-networks.
(2)	Initialization state .
(3)	Select action .
(4)	Perform action, get rewardand next actionthrough environment.
(5)	Put , , , into the experience pool and sample
(6)	Sample , , , in the experience pool for the next action. Let y be the target value
(7)	Update the weights by back propagation mechanism and retrain the Q networks using gradient descent algorithm
(8)	Detect sparse nodes and apply additional reward/penalty updates to nodes
(9)	Update the target networks every steps and copy the current networks parameters to the target networks.