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Learning rate is one of the key hyperparameters of DRL. Fig. shows the performance comparison of MDDQN under different learning rates. When the learning rate is small (LR=0.0005), the convergence speed of MDDQN is slow. With the increase of learning rate (LR=0.005), the convergence speed of MDDQN increases. When the learning rate is too large (LR=0.05), the convergence speed of MDDQN further increases. However, the excessive learning rate makes the agent easy to fall into the local optimal solution. As shown in the figure, the convergence energy consumption of the curve LR=0.05 is higher than that of the low learning rate.
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IEEE/ICACT20230164 Slide.20
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