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Combination of generative artificial intelligence and deep reinforcement learning: performance comparison by Lim, Fang Nie

“…In this study, we explore the integration of Generative Adversarial Networks (GANs) and Deep Reinforcement Learning (DRL) methods, focusing on the performance comparison between different architectures of Sequence Generative Adversarial Networks (SeqGAN) and policy gradient algorithms. We address key challenges in text generation, such as maintaining narrative coherence over long sequences, reducing text repetition, and optimizing SeqGAN for diverse textual outputs. …”
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