A Cloud-Edge Collaborative Multi-Agent Task Offloading Scheme Based on KAN-LSTM Networks

Addressing the computational task offloading chal-lenges, this study develops a cloud-edge cooperative network framework comprising three layers: the user layer, edge layer, and cloud layer. To mitigate the issue of delayed rewards during training, a fixed-time step approach is adopted. In response to the limited robustness of single-agent reinforcement learning within cloud-edge collaborative settings, we introduce a multi-agent task offloading algorithm, designated as KLMAC-PPO, and detail its network architecture. Furthermore, we incorporate LSTM networks to enhance the prediction and management of device mobility. The proposed algorithm utilizes a KAN to derive an optimal offloading strategy. Experimental evaluations demonstrate that the distributed offloading approach significantly ameliorates task delay, reduces energy consumption, and lowers the drop rate under high-load scenarios.