A Software Architectural Model for Generative Artificial Intelligence with Reinforcement Learning

Generative Artificial Intelligence (Gen AI) and Reinforcement Learning (RL) are cutting edge technologies, in the field of AI. This study presents an architectural framework that combines these technologies to improve decision making in systems. Through case studies we explore how integrating AI with RL enhances performance in real world applications. By incorporating RL strategies, our approach ensures parameter settings and successful operation of the AI model leading to results. This framework can be applied in multiple software applications like information synthesis and creative simulation. Despite challenges like training instability and limited resources that generative models face, our research recognises the potential that this approach can effectively addess these issues. Our innovative framework uses RL techniques to improve the models performance during training. The results suggest that combining RL with AI not overcomes existing limitations but also opens up possibilities, for data driven applications. This hybrid method pushes the boundaries of models offering solutions across different fields.