SV2G-ET: A Secure Vehicle-to-Grid Energy Trading Scheme Using Deep Reinforcement Learning

Algorithm 1

Deep Q-Network.

	Input: Energy price, vehicle volume, Reward
	Output: Deep Q-Network parameters.
(1)	DNN parameters is initialized randomly.
(2)	, a target parameter is initialized.
(3)	for epsiode = 1 to do.
(4)	Get .
(5)	for timestep t = 1 to do.
(6)	With probability select action randomly.
(7)	Select action and reward is calculated to proceed to the next state, i.e., .
(8)	State transition details (, , , ) are stored.
(9)	Calculate loss function
(10)	Update the parameter using Gradient Descent algorithm
(11)	For each N, copy weights from to
(12)	end for
(13)	end for