Joint Optimization of Jamming Link and Power Control in Communication Countermeasures: A Multiagent Deep Reinforcement Learning Approach

Algorithm 1

Decentralized allocation of communication jamming resource based on MASAC.

• Initialize the CRB.

• Initialize policy network , twin evaluation network and for each JE i with weights 、、, respectively.

• Initialize twin target network and for each JE with weights 、, respectively.

• Training episode =1.

• While Training episode ≤ Edo

•  Initialize the environment state S(t) = (0, 0, …, 0).

•   for time step t =1, 2, …, T_max

•    Each JE i selects the jamming action according to the current observation .

•     Obtain and carry out the joint jamming action a_t, , then each JE i obtains the shared reward r_t and achieves the next observations .

•     The experience from all JEs is stored in CRB:

•     If the capacity of CRB is larger than β, then the training process begins:

•      Stochastically Sampling mini-batch of experiences from CRB.

•      for each JE i =1, 2, …, N

•        Update the weight and of twin evaluation network with (23)

•        Update the weight of the policy network by (24) and (25)

•        Soft update the weight and of twin target network through (26)

•      end for

•   end If

•  end for

• end while