Multi-Agent deep reinforcement learning based task offloading and caching in computing power network

Abstract Computing power network (CPN) can alleviate the resource bottleneck in mobile edge computing (MEC). In this paper, we investigate the caching-assisted CPN system for task offloading. A combination of policies such as task offloading and caching is considered to reduce the energy consumption of the system under the condition of meeting a certain delay. Besides, we propose an advanced deep reinforcement learning (DRL) algorithm called Request Sensitive Multi Agent Soft Actor-Critic (RS-MASAC) algorithm to achieve minimal system energy consumption by obtaining the optimal policy. Simulation results show that our proposed algorithm has better performance than baseline algorithms.