Leveraging Transformer Architectures for Enhancing Deep Reinforcement Learning: Advancements, Challenges, and Future Directions

The integration of Transformer architectures into Deep Reinforcement Learning (DRL) has recently gained significant attention due to its potential to enhance sequential decision-making and representation learning. This paper presents a structured review of key technical foundations, including the principles of DRL, essential components of Transformer models, and their integration potential. Transformer-based DRL methodologies are categorized into three major areas: single-modal sequential decision-making, cross-modal fusion architectures, and efficiency optimization techniques. These approaches demonstrate the capacity of Transformers to model long-range dependencies, process diverse input modalities, and improve training stability and sample efficiency. Applications in data science are also examined, with a particular focus on financial trading, healthcare decision support, and recommendation systems, showcasing the practical utility of these hybrid approaches. Despite these advancements, notable challenges persist, such as algorithmic complexity, theoretical gaps, and ethical and practical considerations. The paper concludes with a discussion on future directions, emphasizing the need for more interpretable models, efficient training strategies, and responsible deployment of Transformer-enhanced DRL systems.