Deep reinforcement learning-based task offloading and service caching in vehicular edge computing

As a promising solution, Vehicle Edge Computing (VEC) addresses the challenge of driverless technology to process computationally intensive tasks in a latency-sensitive manner by offloading the vehicle's computationally intensive tasks to MEC servers or the cloud. However, in situations with limited server resources, reducing service latency and improving the efficiency of service request processing remains a challenging task. To tackle this problem, we propose a joint task offloading and service caching framework aimed at minimizing the cost of unmanned vehicles. Initially, we formulate the problem as a mixed-integer nonlinear programming problem, subsequently transforming it into a solvable Partially Observable Markov Decision Process (POMDP) problem. We then design and introduce a task offloading algorithm framework based on DDQNL to address this problem. The performance of the proposed algorithm is validated through comparisons with other baseline algorithms.