Dynamic power allocation in IIoT based on multi-agent deep reinforcement learning

agent to learn optimal allocation policy. A centralized training and distributed execution learning framework is proposed, the influence of parameter transmission delay on allocation policy is considered. The designed state space and reward function can adapt to large-scale networks on account of their expandability. Simulation results show the effectiveness and superiority of the proposed method in terms of the system sum rate.