To address the joint latency-energy optimization problem arising from task offloading and coordinated scheduling of “computation-communication-energy” multiple resources during the digitalization

decentralization

and intelligent evolution of distribution networks

a data-driven three-layer collaborative computing model encompassing local terminals

edge servers

and the cloud is developed. With a weighted delay-energy-fairness objective function

the model comprehensively characterizes key factors such as wireless channel conditions

transmission rates

and CPU frequencies

thereby quantifying the impact of multi-resource coordination on system performance. To tackle the challenge of a hybrid action space composed of discrete offloading decisions and continuous bandwidth

computation

and energy allocation

a hybrid deep reinforcement learning (HDRL) framework is proposed. In this framework

a double deep Q-network (DDQN) is employed at the upper layer to select offloading actions

while a deep deterministic policy gradient (DDPG) algorithm is used at the lower layer for continuous resource scheduling. An improved prioritized experience replay (IPER) mechanism is further designed to enhance sample utilization efficiency and convergence speed. Simulation results demonstrate that

compared with pure local computing

pure edge computing

random offloading

genetic algorithms (GA)

and the DDQN+DDPG method without IPER

the proposed HDRL approach significantly reduces average system delay and total energy consumption across multiple scenarios. Moreover

it maintains high fairness as the number of users increases

exhibiting superior scalability robustness

improved task completion rates

and enhanced algorithm stability. The proposed method thus provides a feasible and efficient solution for multi-resource coordinated optimization in distribution networks.