A Reinforcement Learning paradigm for Generative AI Applications in Business

Generative Artificial Intelligence (GenAI) is revolutionizing content creation and natural language comprehension, with large language models (LLMs) and AI generating original content in various modalities, such as text, photos, audio, and videos. Machine Learning (ML) with a hypothesis-based, goal-oriented approach called Reinforcement Learning (RL) that accomplishes long-term objectives through leveraging interactions with the environment. This study aims to establish the Reinforcement Learning (RL) paradigm for GenAI applications in business, using Python to implement RL Q-Learning and the Asynchronous Advantage Actor-Critic (A3C) algorithms. The pragmatism-based study used a positivist, quantitative approach to identify the RL paradigm for GenAI business applications, and was also explanatory, exploratory, and descriptive. An analysis of GenAI’s business applications was done using the 370-person sample size of the study population comprising staff members and clients of five commercial banks in Zimbabwe. The A3C algorithm was found to be simpler and perform better in the Deep Reinforcement Learning (DRL) task. The A3C algorithm, which is premised on Q-Learning in DRL, can be used to combat cybersecurity risks. Because Generative AI can automate tasks that take 60–70% of an employee’s time, it has the potential to dramatically improve the global economy, especially in the banking, high-tech, and life sciences sectors.