###### Abstract  
5G New Radio (NR)-V2X, standardized by 3 GPP Release 16, includes a distributed resource allocation Mode, known as Mode 2, that allows vehicles to autonomously select transmission resources using either sensing-based semi-persistent scheduling (SB-SPS) or dynamic scheduling (DS). In unmanned 5G-NR-V2X scenarios, SB-SPS loses effectiveness with aperiodic and variable data. DS, while better for aperiodic traffic, faces challenges due to random selection, particularly in high traffic density scenarios, leading to increased collisions. To address  
introduces a mandatory re-evaluation mechanism that includes both the SB-SPS and DS schemes. The SB-SPS scheme is inherited from LTE-V2X with minor modifications, while the DS scheme is a new reservation-less approach that requires selecting new transmission resources for each generated message [1; 5; 6]. To date, only a few studies have examined the performance of the SB-SPS scheme in 5G-NR-V2X Mode 2, revealing that increased vehicular traffic density and aperiodic traffic with varying data sizes lead to inefficiencies in the SB-SPS method. These inefficiencies result in poor radio resource utilization (RRU) and a higher probability of resource collisions, particularly when a vehicle fails to transmit a message in an intended slot due to a collision. To address these limitations, novel artificial intelligence (AI)-based solutions and alternative techniques have been proposed. Conversely, there has been limited analysis of the DS scheme. Machine learning has proven invaluable across various domains, including vehicular communications, offering optimal solutions through advanced algorithms. Several studies have employed machine learning to address challenges in V2X communications, such as optimizing resource allocation in C-V2X Mode 4 to mitigate network contention and reducing the signaling overhead in C-V2X Mode 3 [5]. Cutting-edge AI techniques, including machine learning and deep learning, are providing innovative solutions to wireless communication challenges, particularly through the application of deep neural networks in supervised, unsupervised, and reinforcement learning settings [6; 7]. Despite the extensive efforts to apply machine learning techniques to address the challenges in 5G-NR-V2X Mode 2, achieving significant breakthroughs remains challenging [8; 9; 10]. This paper presents a novel approach to resolving persistent collision issues in 5G-NR-V2X Mode 2 by implementing the SEAC reinforcement learning (RL) technique. Our research exclusively focuses on optimizing Mode 2 communication within the European V2X framework, particularly for Cooperative Awareness Messages (CAMs) and Collective Perception Messages (CPMs), which are safety-critical and transmitted more frequently than other message types. Our objective is to reduce collisions during resource reselection and to enhance packet reception ratios. The remainder of this paper is organized as follows: Section 2 reviews the literature on SB-SPS and DS, as well as related work. Section 3 introduces the proposed SEAC technique, and the methodology employed. Section 4 describes the simulation environment, Section 5 presents the results and their analysis, and Section 6 concludes the paper. The main findings of this paper are as follows:  
* The performance of the SB-SPS scheme can significantly deteriorate in the presence of aperiodic traffic, especially with increasingly stringent packet delay budget (PDB) constraints.