A Resource Allocation Scheme for Intelligent Tasks in Vehicular Networks

Algorithm 2

Multiagent DQN algorithm.

1: Input: action space , state space , learning factor , discount factor , and other system parameters;

2: Onput: Target Q-network .

3: Initialization Process:

4: for each agent do

5:   Initialize replay buffer ;

6:   Initialize the weights of Q-network ;

7:   Initialize the weights of target Q-network .

8: end for

9: Learning Process:

10: for each agent do

11:   repeat

12:      Initialization state

13:    fordo

14:      Generate a random number ;

15:      ifthen

16:        Select a random action from ;

17:      else

18:        Select the action .

19:      end if

20:      Perform action ;

21:      Get new state and reward by Equation (11);

22:      Store transition in ;

23:      Sample random transitions from ;

24:      

25:      Use as a loss function to train Q-network;

26:      Updata the weights: ;

27:      ;

28:      , after steps.

29:    end for

30:   until converges.

31: end for