[Retracted] Feudal Multiagent Reinforcement Learning for Interdomain Collaborative Routing Optimization

Algorithm 1

Feudal Multiagent Actor-Critic for collaborative routing.

Input: Initialize inter-domain environments with agents contained managers and workers

Output: All managers’ and workers’ routing policies

1: for each episode to do

2: Initialize a random process for routing actions exploration, get the initial state

3: for each time-step to do

4:  Managers select an action under the current policy and exploration

5:  Workers select and execute an action

6:  Receive the reward , and observe the next newly state

7:  Store replay buffer and

8:  for each agent to do

9:   Sample a random minibatch from and

10:   Update actor by using policy gradient:

11:     

12:   Update critic by minimizing the loss:

13:     

14:   end for

15:  Update target network parameters:

16:   

17: end for

18: end for