Deep Reinforcement Learning for Scheduling in an Edge Computing-Based Industrial Internet of Things

Algorithm 1

Procedures of DDQN-based DISA.

1. Initialize the evaluate network with random weights and biases as ;

2. Initialize the target network as a copy of the evaluate network weights and biases as ;

3. Initialize replay memory ;

4. for i=1 to do

5. Initialize state in equation (6);

6. Input the system state into the evaluate DQN;

7. Compute the value ;

8. With probability , choose an action ;

9. Execute action , receive a reward and observe the next state ;

10. Store interaction tuple () in ;

11. for j =1 to do

12.  Sample a random transition from ;

13.  Compute the target value

   ;

14.  Train the network to minimize the loss function

   ;

15.  Perform gradient descent with respect to ;

16.  Update target networks every steps

   ;

17. end for

18. end for