###### Abstract  
With the widespread deployment of various emerging intelligent applications, information timeliness is crucial for intelligent decision-making in vehicular networks, where vehicular edge computing (VEC) has become an important paradigm to enhance computing capabilities by offloading tasks to edge nodes. To promote the information timeliness in VEC, an optimization problem is formulated to minimize the age of information (AoI) by jointly optimizing task offloading and subcarrier allocation. Due to the time-varying channel and the coupling of the continuous and discrete optimization variables, the problem exhibits non-convexity, which is difficult to solve using traditional mathematical optimization methods. To efficiently tackle this challenge, we employ a hybrid proximal policy optimization (HPPO)-based deep reinforcement learning (DRL) method by designing the mixed action space involving both continuous and discrete variables. Moreover, an action masking mechanism is designed to filter out invalid actions in the action space caused by limitations in the effective communication distance between vehicles. As a result, a mask-assisted HPPO (MHPPO) method is proposed by integrating the action masking mechanism into the HPPO. Simulation results show that the proposed MHPPO method achieves an approximately 28.9% reduction in AoI compared with the HPPO method and about a 23% reduction compared with the mask-assisted deep deterministic policy gradient (MDDPG). v 2025 20  
and alleviate local computational burdens, which makes it an indispensable component of future IoV systems. The joint optimization of computation offloading and resource allocation in VEC has made significant progress. Some existing studies primarily focus on optimizing latency [6, 7], energy consumption [8, 9], the trade-off between latency and energy consumption [10, 11], or utility functions [12, 13]. In VEC networks, latency is a crucial metric for evaluating the efficiency of task processing, which directly impacts the timeliness of real-time applications. Many existing latency-related studies on VEC networks mainly study vehicle-to-infrastructure (V2I) offloading strategies [14, 15, 16, 17, 18, 19]. That is, in the aforementioned studies, the tasks were only allowed to be offloaded to the RSUs over V2I links for computing, where vehicle-to-vehicle (V2V) links were not involved for offloading tasks to vehicles with idle resources. Motivated by this, [20, 21, 22, 23, 24, 25] integrated V2I offloading with V2V offloading to improve the network performance further. [PERSON] et al. [20] proposed a tabu search-based matching method to minimize the delays and energy consumption by jointly optimizing offloading ratios, target selection, channel allocation, mode selection, and computation resource allocation coefficients. [PERSON] et al. [21] proposed an algorithm based on generalized Benders decomposition to minimize the total task processing delays by jointly optimizing the task offloading, allocation of wireless channels, and distribution of computing resources. [PERSON] et al. [22] proposed a distributed algorithm based on coalition formation games to minimize the total task completion delay in VEC networks by jointly optimizing sub-channel allocation, power control, and bandwidth allocation policies. [PERSON] et al. [23] proposed a Deep Q-Network-based method to maximize the delay and energy consumption utility by optimizing the offloading decision. [PERSON] et al. [24] proposed a novel RSU-assisted learning-based method to minimize the task execution delay by jointly optimizing offloading ratio and offloading decision. [PERSON] et al. [25] proposed deep reinforcement learning (DRL)-based method for the ISAC-assisted VEC network to minimize the delay and energy consumption by jointly optimizing offloading ratio and proportion of bandwidth.