Combination of generative artificial intelligence and deep reinforcement learning: performance comparison
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 ke...
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| Format: | Final Year Project / Dissertation / Thesis |
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2024
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| Online Access: | http://eprints.utar.edu.my/7444/1/2200481_LIM_FANG_NIE.pdf http://eprints.utar.edu.my/7444/ |
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| _version_ | 1855616581233541120 |
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| author | Lim, Fang Nie |
| author_facet | Lim, Fang Nie |
| author_sort | Lim, Fang Nie |
| building | UTAR Library |
| collection | Institutional Repository |
| content_provider | Universiti Tunku Abdul Rahman |
| content_source | UTAR Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | 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. The study incorporates architectural
innovations like Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) that enhance the ability of SeqGAN to capture long-range dependencies in sequences, while attention mechanisms improve contextual awareness by
selectively focusing on relevant parts of the sequence. Through extensive experiments, we analyze the influence of various neural network configurations and regulatory mechanisms, including gradient penalties and regularization on the quality of the generated text. Our findings show a 15% increase in BLEU scores, highlighting significant improvements in text coherence and diversity across various datasets, demonstrating the effectiveness of integrating SeqGAN with policy gradient methods for automated content generation.
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| format | Final Year Project / Dissertation / Thesis |
| id | my-utar-eprints.7444 |
| institution | Universiti Tunku Abdul Rahman |
| publishDate | 2024 |
| record_format | eprints |
| spelling | my-utar-eprints.74442026-01-14T17:11:39Z Combination of generative artificial intelligence and deep reinforcement learning: performance comparison Lim, Fang Nie QA75 Electronic computers. Computer science T Technology (General) 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. The study incorporates architectural innovations like Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) that enhance the ability of SeqGAN to capture long-range dependencies in sequences, while attention mechanisms improve contextual awareness by selectively focusing on relevant parts of the sequence. Through extensive experiments, we analyze the influence of various neural network configurations and regulatory mechanisms, including gradient penalties and regularization on the quality of the generated text. Our findings show a 15% increase in BLEU scores, highlighting significant improvements in text coherence and diversity across various datasets, demonstrating the effectiveness of integrating SeqGAN with policy gradient methods for automated content generation. 2024 Final Year Project / Dissertation / Thesis NonPeerReviewed application/pdf http://eprints.utar.edu.my/7444/1/2200481_LIM_FANG_NIE.pdf Lim, Fang Nie (2024) Combination of generative artificial intelligence and deep reinforcement learning: performance comparison. Final Year Project, UTAR. http://eprints.utar.edu.my/7444/ |
| spellingShingle | QA75 Electronic computers. Computer science T Technology (General) Lim, Fang Nie Combination of generative artificial intelligence and deep reinforcement learning: performance comparison |
| title | Combination of generative artificial intelligence and deep reinforcement learning: performance comparison |
| title_full | Combination of generative artificial intelligence and deep reinforcement learning: performance comparison |
| title_fullStr | Combination of generative artificial intelligence and deep reinforcement learning: performance comparison |
| title_full_unstemmed | Combination of generative artificial intelligence and deep reinforcement learning: performance comparison |
| title_short | Combination of generative artificial intelligence and deep reinforcement learning: performance comparison |
| title_sort | combination of generative artificial intelligence and deep reinforcement learning: performance comparison |
| topic | QA75 Electronic computers. Computer science T Technology (General) |
| url | http://eprints.utar.edu.my/7444/1/2200481_LIM_FANG_NIE.pdf http://eprints.utar.edu.my/7444/ |
| url_provider | http://eprints.utar.edu.my |