# Machine Learning for Decision Support and Automation in Games: A Study on Vehicle Optimal Path
## 1 Introduction  
In the dynamic and rapidly evolving field of gaming artificial intelligence (AI), developing sophisticated autonomous agents has become a central goal, particularly in complex and interactive environments [1]. One of the most promising approaches to achieving advanced decision-making and automation in games involves deep reinforcement learning (DRL), a technique that merges the depth of deep learning (DL) with the goal-oriented capabilities of Reinforcement Learning (RL). These two conventional approaches are combined to overcome the limitations of RL in complex environments with large state spaces [2]. DRL-based algorithms deliver outstanding performance; however, the frequent need for evaluation significantly contributes to computational overload, resulting in notable decision delays and a decline in overall system performance [3]. The use of DRL in simulating autonomous driving within a game setting offers a unique opportunity to test and refine AI methodologies in an environment that mimics real-world dynamics. However, unlike standard toy problems often used in experimentation, the environment addressedin this paper simulates a real-world scenario with greater complexity and computational demands. This increased complexity enables a more rigorous evaluation of reinforcement learning algorithms like Proximal Policy Optimization (PPO) and Soft Actor-Critic (SAC), pushing them closer to real-world application limits. Through this, we aim to demonstrate whether these models are capable of addressing real-world problems or remain constrained to controlled research environments. Unlike many recent studies, which focus primarily on theoretical DRL techniques and sensor integration [4], or explore different system architectures for autonomous driving [5], this work diverges by offering a simulation environment that closely approximates real-world conditions. While approaches like Model-Based Imitation Learning for Urban Driving (MILE) [6] represent steps toward reality, they utilize different methods and goals. Similarly, other works, such as the Performance Analysis of Deep Neural Network Controllers for Autonomous Driving using Nonlinear Model Predictive Control [7], focus on specific control strategies. In some cases, DRL has also been applied to trajectory planning problems, such as in the DRL-QiER framework, which combines DRL with quantum-inspired experience replay to optimize UAV mobility while minimizing communication outages [8]. Unlike DRL-QiER, which addresses UAV-specific challenges like airspace boundaries and wireless connectivity, this research focuses on terrestrial navigation with unique constraints, such as realistic motorcycle physics and complex terrain-based obstacles. In contrast, this research takes a more holistic approach by testing DRL techniques in a gaming environment that mirrors real-world driving dynamics. This allows for the evaluation of both decision-making efficiency and adaptability in a more realistic setting. Although a significant design challenge for these scenarios is the transfer of learning, as the experience of playing a game differs greatly from driving a vehicle [9], this controlled setting allows for rapid experimentation and iteration, which is essential for advancing AI technologies and transferring learned lessons into real-world situations [10]. This study undertakes a comprehensive examination of DRL techniques to evaluate their effectiveness in improving the learning efficiency, decision-making accuracy, and adaptability of autonomous car agents in dynamic and potentially unpredictable gaming scenarios. While DRL offers significant advantages to these scenarios, challenges remain in effectively learning from sparse, high-dimensional sensory inputs and ensuring safety and robustness in real-world driving scenarios. To address these challenges, increasing model complexity aims to enhance adaptability to intricate situations and improve generalization capacity.