Abstract: Computation offloading strategy in mobile edge computing can help users decide how to execute tasks, which is related to user experience, and has become a research hotspot in mobile edge computing. At present, most computation offloading strategies are carried out under the condition of overall task offloading, and only consider a single indicator of delay or energy consumption, and do not combine the two for optimization. To solve this problem, this paper takes the weighted sum of task processing delay and energy consumption as the optimization goal, and proposes a partial offloading algorithm based on reinforcement learning. We divide the processing of a single task into local computing and partial offloading computing, and introduce a variable to determine the offloading weight in partial offloading. Finally, we use reinforcement learning Q-learning to complete the computation offloading and resource allocation of all tasks. Experimental results show that the proposed algorithm can effectively reduce the delay and energy consumption of task processing.

Key words: mobile edge computing, computation offloading, reinforcement learning, Q-learning