Implementing Deep Reinforcement Learning (DRL)-based Driving Styles for Non-Player Vehicles

The integration of Deep Reinforcement Learning into serious game development offers a transformative approach to creating realistic non-player vehicle behavior. By utilizing a hierarchical architecture that delegates low-level control to existing Advanced Driver Assistance Systems while reserving high-level strategic decision-making for the learning agent, developers can achieve superior safety and speed metrics. This methodology, powered by the Proximal Policy Optimization algorithm, significantly outperforms traditional heuristic-based systems, reducing training time by over an order of magnitude and eliminating the need for manual, labor-intensive coding of driving behaviors.

The research establishes that distinct driving styles—categorized as Comfort, Standard, and Aggressive—are effectively generated by dynamically adjusting reward function weights. This flexibility allows for a diverse range of realistic traffic interactions within highway simulation environments. Furthermore, the implementation of curriculum learning serves as a critical optimization technique, enabling specialized agents to inherit knowledge from baseline models. This approach mitigates the inherent complexity of training agents from scratch, ensuring faster iteration cycles and more robust performance in complex virtual scenarios.

This study focuses on the intersection of autonomous vehicle simulation and game design, specifically targeting the enhancement of non-player character realism. By leveraging established machine learning frameworks and high-level decision-making architectures, the findings provide a scalable solution for developers seeking to improve the fidelity of simulated traffic. The resulting framework offers a sophisticated, efficient alternative to conventional programming methods, providing a foundation for more immersive and responsive virtual environments in the serious games industry.