## 1 Introduction
After receiving transmitted signals, the user equipment adjusts the angles and feeds them back to the transmitter to predict the next angle. While DL-based beam tracking has shown promising results, constructing datasets is challenging, significantly increasing deployment costs. In reinforcement learning (RL), agents improve their decisions through trial and error by interacting with the environment, making it suitable for unknown environments and a more attractive technology [17; 18]. [PERSON]. [19; 20; 21] used RL to predict pilot directions to enhance pilot-based channel estimation accuracy. However, using pilots is unsuitable for high-mobility scenarios. In contrast, [PERSON]. [22; 23; 24; 25] suggests using RL to predict the beam direction for the next slot directly, reducing pilot-related overhead. Specifically, [PERSON]. [22; 23] employed Q-learning to predict beam directions based on historical channel states, but this method struggles in complex environments due to the difficulty of maintaining high-dimensional Q-tables. [PERSON]. [24; 25] utilized deep reinforcement learning (DRL) for prediction, leveraging feature extraction from high-dimensional data to compress state and action spaces, making it suitable for environments with large state and action spaces. Most of the aforementioned methods are based on pilot and uplink feedback mechanisms, which involve substantial time overhead. In this context, researchers have introduced ISAC technology, utilizing mmWave signals with ISAC functions to achieve a lower latency beam allocation, which has been widely studied in recent years [26; 27; 28]. Moreover, ISAC technology offers significant advantages in improving spectrum efficiency and reducing equipment costs [29]. In [2], the authors propose using the Extended Kalman Filter (EKF) for ISAC beam tracking, accurately tracking and predicting the target's motion parameters with sensing data without additional pilot overhead. Building on this, [PERSON]. [30] added the feedback of the target vehicle's angle of arrival (AOA) and speed, achieving better beam tracking accuracy on curved road trajectories. These studies correct the beam direction in each time slot to maintain precise beam pointing, but this may introduce unnecessary time overhead. In [3], the authors propose using ISAC signals to sense and predict the target's motion parameters, combined with the factor graph message passing algorithm to improve tracking accuracy, and then designing the transmit beamformer based on the predicted angles to establish a reliable communication link. Although this scheme achieved good beam tracking accuracy, the authors did not consider a dynamic transmit power allocation. Given that EKF uses a first-order Taylor series approximation, its accuracy is limited. To address beam tracking with UAV jitter, Ref. [31] used the Unscented Kalman Filter (UKF) with second-order accuracy, combining ISAC sensing information to achieve more accurate beam direction predictions. However, most of these schemes employ idealized road models, which degrade their performance on real-world road trajectories. To enhance algorithm generalization, researchers have combined ISAC with DL. In [32], the authors input ISAC signal echoes into a deep neural network (DNN) for training, with the network outputting the predicted beam direction for the next time slot. In [14], convolutional neural networks (CNN) and LSTM networks extracted spatiotemporal features from ISAC echoes, directly outputting the predicted downlink beamforming matrix to maximize the sum achievable rate under sensing constraints. While these schemes achieve good results, they also face high dataset construction costs and do not consider resource allocation issues. In this paper, we propose a novel algorithm named IMSBA that combines ISAC technology with MAPPO and uses Stackelberg game theory for joint power allocation between I2V and V2V. Specifically, the algorithm first optimizes the beam directions and frequency band allocation for V2V vehicles using MAPPO.