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A solution for Dynamic Spectrum Management in Mission-Critical UAV Networks using Team Q learning as a Multi-Agent Reinforcement Learning Approach

Python 100.00%
ai reinforcement-learning marl qlearning-algorithm multiagent-reinforcement-learning spectrum uav drone wildfire

reinforcement_learning_team_q_learnig_marl_multi_agent_uav_spectrum_task's Issues

i found some question

when i run it ,it have some problems
File "E:/python_code/Reinforcement_Learning_Team_Q_learnig_MARL_Multi_Agent_UAV_Spectrum_task-main/Reinforcement_Learning_Team_Q_learnig_MARL_Multi_Agent_UAV_Spectrum_task-main/main.py", line 174, in
csi_coef = get_csi(num_UAV, loc_dict, X_Mat[Step, Eps, :], Y_Mat[Step, Eps, :])
File "E:\python_code\Reinforcement_Learning_Team_Q_learnig_MARL_Multi_Agent_UAV_Spectrum_task-main\Reinforcement_Learning_Team_Q_learnig_MARL_Multi_Agent_UAV_Spectrum_task-main\csi.py", line 158, in get_csi
csi_h[:, source_uav] = np.squeeze(h_S_uav)
ValueError: could not broadcast input array from shape (2,2) into shape (2)

could you help me?

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