Abstract

Abstract

Abstract

Abstract

Abstract

Abstract
This paper presents an interactive game designed to improve users’ experience related to driving behaviour, as well as to provide decision support in this context. This paper explores machine learning (ML) methods to enhance the decision-making and automation in a gaming environment. It examines various ML strategies, including supervised, unsupervised, and Reinforcement Learning (RL), emphasizing RL’s effectiveness in interactive environments and its combination with Deep Learning, culminating in Deep Reinforcement Learning (DRL) for intricate decision-making processes. By leveraging these concepts, a practical application considering a gaming scenario is presented, which replicates vehicle behaviour simulations from real-world driving scenarios. Ultimately, the objective of this research is to contribute to the ML and artificial intelligence (AI) fields by introducing methods that could transform the way player agents adapt and interact with the environment and other agents decisions, leading to more authentic and fluid gaming experiences. Additionally, by considering recreational and serious games as case studies, this work aims to demonstrate the versatility of these methods, providing a rich, dynamic environment for testing the adaptability and responsiveness, while can also offer a context for applying these advancements to simulate and solve real-world problems in the complex and dynamic domain of mobility. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.