Exploring Reinforcement Learning for Optimizing Generative AI Systems

Abstract

This paper explores the optimization of generative AI models using Reinforcement Learning (RL) strategies. Generative AI, such as GANs and VAEs, has seen remarkable progress, but challenges like unstable training, mode collapse, and limited diversity persist. RL offers a promising solution by introducing structured decision-making and long-term reward optimization into the generative process. We propose a framework where RL algorithms, such as Proximal Policy Optimization (PPO) and Deep Q-Networks (DQN), guide the training of generative models by optimizing a reward function tailored to improve output quality and diversity. Experimental results on standard datasets demonstrate that RL- enhanced generative models outperform traditional models in terms of stability, convergence speed, and sample diversity. We also discuss challenges like reward design and computational complexity, proposing directions for future research, such as hybrid approaches and multi- agent RL for collaborative generation. This work highlights the potential of RL to push generative AI beyond current limitations.