Algorithm 1. QL_MAS

Initialize: The capacity Cap of the memory M, the values Q: ∀r, a|Q (r, a) = 0, The estimation weight  LSTM-DQN θ = θ_0, the weight of the LSTM-DQN objectives θ′  For episode = 1 → ep do # ep represents the number of episodes   Fix the initial positions of the agents according to the map from the RAG   For i = 1 -> Reg do # Reg is the number of regions    #N is the number of agents N = Reg    Implement in each Reg i an agent Ai   End for   Do as long as t < Cap    For j = 1 -> N do     Calculation of the initial actions (2)     Calculation of initial Q-value (1)     Verification of the best adjacent neighbors satisfying the similarity criteria     Negotiation to decide the optimal proposal    End for     Fusion     Update of the map of the regions     Update Reg     N = Reg    For j = 1 -> N do     Calculation of the actions (2)     Calculation of Q-value (1)     Calculation of reward (4)     Next state calculation (3)     Save data d = {state(t), action(t), R(t), state(t + 1)} in memory M     End for    End Do   Reset  End for