Efficient Delay-Sensitive Task Offloading to Fog Computing with Multi-Agent Twin Delayed Deep Deterministic Policy Gradient

Fog computing presents a significant paradigm for extending the computational capabilities of resource-constrained devices executing increasingly complex applications. However, effectively leveraging this potential critically depends on the implementation of efficient task offloading mechanisms to proximal fog nodes, particularly under conditions of high resource contention. To address this challenge, we introduce MAFCPTORA (multi-agent fully cooperative partial task offloading and resource allocation), a decentralized multi-agent deep reinforcement learning algorithm for cooperative task offloading and resource allocation. We evaluated the performance of MAFCPTORA and compared it against recent approaches. MAFCPTORA demonstrated superior performance compared to recent methods, achieving a significantly higher average reward (0.36 ± 0.01), substantially lower average latency (0.08 ± 0.01), and reduced energy consumption (0.76 ± 0.14).