Collaborative Multi-Agent Deep Reinforcement Learning for Energy-Efficient Resource Allocation in Heterogeneous Mobile Edge Computing Networks

Mobile edge computing (MEC) is an enabling technology for next-generation network architectures to deliver more diversified communication services and meet more demanding quality-of-service (QoS) requirements. However, owing to the growing scale and heterogeneity of the networks, energy-efficient resource allocation in heterogeneous MEC (Het-MEC) networks faces great challenges. As an emerging research area, multi-agent deep reinforcement learning (MADRL) is expected to realize autonomous resource allocation in Het-MEC networks by learning from trial and error. Nevertheless, existing MADRL-based solutions are usually not applicable to practical scenarios by the limitations of centralized control schemes or massive signaling overhead. To address the issue, we first formulate the energy-efficient resource allocation problem in Het-MEC networks as a time-variant mixed-integer nonlinear programming (MINLP) problem. Then, we propose a fully decentralized collaborative MADRL-based algorithm featuring the multi-actor shared-critic (MASC) architecture and the regional training distributed execution (RTDE) framework, which effectively stabilizes model training and reduces information exchange, respectively. Finally, we conduct extensive simulations to evaluate the performance of the proposed algorithm. Numerical results demonstrate that the proposed algorithm comprehensively outperforms several mainstream baseline methods in terms of convergence performance, scalability, and robustness.