A Multiagent Reinforcement Learning Solution for Geometric Configuration Optimization in Passive Location Systems

Algorithm 1

Geometric configuration optimization with multiagent reinforcement learning.

(1)	Initialize the DPD passive location system with target transmitter emitting signals, specify the number of stations and the central station ;
(2)	Initialize neural network parameters ,
(3)	Initialize the iteration counter .
(4)	repeat
(5)	for do
(6)	Intercept the signals ;
(7)	Send the state to the central station;
(8)	end for
(9)	The central station intercepts signals and send to vice stations;
(10)	Update the parameters of value networks:
	;
(11)	for all  do
(12)	Update the parameters of policy network:
	;
(13)	end for
(14)	Update the counter ;
(15)	until the task is completed or reaching the maximum of counter