DTA-RL: Dynamic Topology Adaptive Reinforcement Learning Approach for Task Offloading in Mobile Edge Computing

Mobile edge computing (MEC) enhances data processing by enabling users to offload tasks to edge servers with enough computation resource. In multi-user and multi-server scenario, the offloading scheduling is overwhelming complex and significantly influences the processing delay, which makes deep learning (DL) become an appealing approach. Yet, prior DL-based methods often overlook dynamic topology challenges due to the inflexibility of fixed neural network structures, leading to constrained performance. To tackle this challenge, a novel reinforcement learning framework named dynamic topology adaptive reinforcement learning (DTA-RL) is proposed in this paper. The MEC network is modeled as a graph based on the communication relationships between users and servers, and the offloading process is formulated as a Markov decision process (MDP). Building on the graph model and MDP, DTA-RL leverages graph attention networks to handle dynamic observation spaces and incorporates an attention mechanism for decision-making in environments with evolving action spaces. Simulation results illustrate that DTA-RL effectively reduces task processing delays and offloading failure rates within the MEC system. Furthermore, the pre-trained model can be seamlessly implemented in networks with new topology without experiencing significant performance degradation. The code is available at https://github.com/UNIC-Lab/DTA-RL.