Edge intelligence capitalizes on heterogeneous edge resources to empower artificial intelligence–driven applications while mitigating dependence on centralized cloud infrastructures. In mobile edge computing (MEC) systems, resource scheduling constitutes a latency-critical task within inherently dynamic environments. Deep reinforcement learning (DRL) has been widely employed to address this challenge, as it enables agents to derive task-specific policies through continual interactions with the environment. Nevertheless, MEC environments exhibit substantial heterogeneity, and the relative importance of various resource types may fluctuate as the system evolves, thereby altering task objectives. Consequently, agents often necessitate retraining across new domains, the process that is computation ally intensive and time inefficient. To surmount these challenges, transfer reinforcement learning (TRL) has emerged as a promising paradigm that facilitates the transfer of agents' acquired knowledge from the source domain to the target domain. To the best of our knowledge, this is the first comprehensive survey dedicated to TRL applications in MEC systems. Specifically, we first elucidates the foundational principles of TRL, encompassing problem formulation and DRL methodologies. Subsequently, we systematically reviews and critically analyzes existing research on TRL and its applications in MEC systems. Finally, we discuss persistent technical limitations and delineates promising avenues for future exploration.

As an important branch of the Internet of Things (IoT), vehicular networks play a crucial role in the construction of intelligent transportation systems. However, due to the rapid movement of vehicles and signal obstruction, achieving high- quality and low-latency communication in vehicular networks remains a significant challenge. To address this issue, this paper proposes a novel data communication method based on interval type-2 fuzzy logic and cooperative game theory. Firstly, interval type-2 fuzzy logic is utilized to infer vehicle stability, thereby selecting high-quality backbone nodes. Concurrently, the memory and forgetfulness functions of the Gated Recurrent Unit are employed to retain critical data packets. Subsequently, a greedy algorithm and cooperative game theory model are used to describe the behavior of vehicles in Roadside-to-Vehicle (R2V) communication and Vehicle-to- Vehicle (V2V) communication, respectively. This approach encourages backbone nodes to cooperate and serve other vehicles based on a benefit function. Experimental results demonstrate that the proposed method excels in terms of transmission delay, coverage range, and data packet delivery success rate.

Unmanned Aerial Vehicles (UAVs) can capture pictures of road conditions in all directions and from different angles by carrying high-definition cameras, which helps gather relevant road data more effectively. However, due to their limited energy capacity, drones face challenges in performing related tasks for an extended period. Therefore, a crucial concern is how to plan the path of UAVs and minimize energy consumption. To address this problem, we propose a multi-agent deep deterministic policy gradient-based (MADDPG) algorithm for UAV path planning (MAUP). Considering the energy consumption and memory usage of MAUP, we have conducted optimizations to reduce consumption on both fronts. Firstly, we define an optimization problem aimed at reducing UAV energy consumption. Secondly, we transform the defined optimization problem into a reinforcement learning problem and design MAUP to solve it. Finally, we optimize energy consumption and memory usage by reducing the number of neurons in the hidden layer of MAUP and conducting fine-grained pruning on connections. The final simulation results demonstrate that our method effectively reduces the energy consumption of UAVs compared to other methods.

Edge computing technology can be used to the Internet of Vehicles (IOV) to solve the mobile characteristics of vehicles and the limited communication range between roadside units and vehicles. New method of edge computing-based data adaptive return in IOV is proposed in this article. The transmission strategy can be determined by adaptive estimating the vehicle movement, the amount of data returned, the maximum transmission delay, and effective life of link. And the factors, such as speed, direction, and position of the vehicles, are comprehensively considered and these factors can be measured by the stability effect value when adaptive designing the auxiliary transmission strategy. At the same time, greedy selection method is used when constructing the data return link, and the neighbor node as the relay node with the maximum stability and efficiency value is chosen. Our experimental results show our method in terms of performance on transmission delay and packet delivery rate is better than other ones.