Cloud-Based Reinforcement Learning for Autonomous Systems: Implementing Generative AI for Real-time Decision Making and Adaptation

References

[1] Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. *Advances in Neural Information Processing Systems, 27*, 2672–2680.

[2] Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., ... & Hassabis, D. (2017). Mastering the game of Go without human knowledge. *Nature, 550*(7676), 354-359.

[3] Lillicrap, T. P., Hunt, J. J., Pritzel, A., Heess, N., Erez, T., Tassa, Y., ... & Wierstra, D. (2016). Continuous control with deep reinforcement learning. *Proceedings of the International Conference on Learning Representations (ICLR 2016)*.

[4] Schmidhuber, J. (2015). Deep learning in neural networks: An overview. *Neural Networks, 61*, 85-117.

[5] Mnih, V., Kavukcuoglu, K., Silver, D., Rusu, A. A., Veness, J., Bellemare, M. G., ... & Hassabis, D. (2015). Human-level control through deep reinforcement learning. *Nature, 518*(7540), 529-533.

[6] Sutton, R. S., & Barto, A. G. (1998). *Reinforcement learning: An introduction*. MIT Press.

[7] Kober, J., Bagnell, J. A., & Peters, J. (2013). Reinforcement learning in robotics: A survey. *The International Journal of Robotics Research, 32*(11), 1238-1274.

[8] Kingma, D. P., & Welling, M. (2013). Auto-encoding variational Bayes. *arXiv preprint arXiv:1312.6114*.

[9] Ho, J., & Ermon, S. (2016). Generative adversarial imitation learning. *Advances in Neural Information Processing Systems, 29*, 4565-4573.

[10] Andrychowicz, M., Baker, B., Chociej, M., Jozefowicz, R., McGrew, B., Pachocki, J., ... & Zaremba, W. (2020). Learning dexterous in-hand manipulation. *The International Journal of Robotics Research, 39*(1), 3-20.

[11] Brockman, G., Cheung, V., Pettersson, L., Schneider, J., Schulman, J., Tang, J., & Zaremba, W. (2016). OpenAI Gym. *arXiv preprint arXiv:1606.01540*.

[12] Schmidhuber, J. (1991). A possibility for implementing curiosity and boredom in model-building neural controllers. *Proceedings of the First International Conference on Simulation of Adaptive Behavior from Animals to Animals, 1*, 222-227.

[13] Levine, S., Finn, C., Darrell, T., & Abbeel, P. (2016). End-to-end training of deep visuomotor policies. *Journal of Machine Learning Research, 17*(1), 1334-1373.

[14] Oord, A. V. D., Kalchbrenner, N., & Kavukcuoglu, K. (2016). Pixel recurrent neural networks. *Proceedings of the International Conference on Machine Learning (ICML 2016)*, 1747-1756.

[15] Finn, C., Abbeel, P., & Levine, S. (2017). Model-agnostic meta-learning for fast adaptation of deep networks. *Proceedings of the 34th International Conference on Machine Learning*, 1126-1135.

[16] Murthy, N. P. (2020). Optimizing cloud resource allocation using advanced AI techniques: A comparative study of reinforcement learning and genetic algorithms in multi-cloud environments. World Journal of Advanced Research and Reviews, 7(2), 359–369. https://doi.org/10.30574/wjarr.2020.07.2.0261

[17] Thakur, D. (2020, July 5). Optimizing Query Performance in Distributed Databases Using Machine Learning Techniques: A Comprehensive Analysis and Implementation - IRE Journals. IRE Journals. https://www.irejournals.com/paper-details/1702344

[18] Mehra, A. (2020). Title of the article. International Research Journal of Modernization in Engineering Technology and Science, 2(9), pages. https://www.irjmets.com/uploadedfiles/paper/volume_2/issue_9_september_2020/4109/final/fin_irjmets1723651335.pdf

[19] Krishna, K. (2020). Towards Autonomous AI: Unifying Reinforcement Learning, Generative Models, and Explainable AI for Next-Generation Systems. Journal of Emerging Technologies and Innovative Research, 7(4), 60–61. https://www.jetir.org/papers/JETIR2004643.pdf

[20] Krishna, K. (2021, August 17). Leveraging AI for Autonomous Resource Management in Cloud Environments: A Deep Reinforcement Learning Approach - IRE Journals. IRE Journals. https://www.irejournals.com/paper-details/1702825

[21] Optimizing Distributed Query Processing in Heterogeneous Multi-Cloud Environments: A Framework for Dynamic Data Sharding and Fault-Tolerant Replication. (2021). International Research Journal of Modernization in Engineering Technology and Science. https://doi.org/10.56726/irjmets5524

[22] Thakur, D. (2021). Federated Learning and Privacy-Preserving AI: Challenges and Solutions in Distributed Machine Learning. International Journal of All Research Education and Scientific Methods (IJARESM), 9(6), 3763–3764. https://www.ijaresm.com/uploaded_files/document_file/Dheerender_Thakurx03n.pdf

[23] Krishna, K., & Thakur, D. (2021). Automated Machine Learning (AutoML) for Real-Time Data Streams: Challenges and Innovations in Online Learning Algorithms. In Journal of Emerging Technologies and Innovative Research (JETIR) (Vol. 8, Issue 12). http://www.jetir.org/papers/JETIR2112595.pdf

[24] Mehra, N. A. (2021). Uncertainty quantification in deep neural networks: Techniques and applications in autonomous decision-making systems. World Journal of Advanced Research and Reviews, 11(3), 482–490. https://doi.org/10.30574/wjarr.2021.11.3.0421

[25] Murthy, P. (2021, November 2). AI-Powered Predictive Scaling in Cloud Computing: Enhancing Efficiency through Real-Time Workload Forecasting - IRE Journals. IRE Journals. https://www.irejournals.com/paper-details/1702943

[26] Murthy, P., & Mehra, A. (2021). Exploring Neuromorphic Computing for Ultra-Low Latency Transaction Processing in Edge Database Architectures. Journal of Emerging Technologies and Innovative Research, 8(1), 25–26. https://www.jetir.org/papers/JETIR2101347.pdf

[27] Murthy, P. (2022). Title of the article. International Journal of Scientific Research and Engineering Development (IJSRED), 5(6). http://www.ijsred.com/volume5-issue6-part16.html

[28] Krishna, K., & Murthy, P. (2022). AI-ENHANCED EDGE COMPUTING: BRIDGING THE GAP BETWEEN CLOUD AND EDGE WITH DISTRIBUTED INTELLIGENCE. TIJER - INTERNATIONAL RESEARCH JOURNAL, 9(2). https://tijer.org/tijer/papers/TIJER2202006.pdf

[29] Krishna, K. (2022, August 1). Optimizing query performance in distributed NoSQL databases through adaptive indexing and data partitioning techniques. International Journal of Creative Research Thoughts (IJCRT). https://ijcrt.org/viewfulltext.php?&p_id=IJCRT2208596

[30] Thakur, D. (2022, June 1). AI-Powered Cloud Automation: Enhancing Auto-Scaling Mechanisms through Predictive Analytics and Machine Learning. IJCRT. Retrieved from https://ijcrt.org/viewfulltext.php?&p_id=IJCRT22A6978

[31] Murthy, P., & Thakur, D. (2022). Cross-Layer Optimization Techniques for Enhancing Consistency and Performance in Distributed NoSQL Database. International Journal of Enhanced Research in Management & Computer Applications, 35. https://erpublications.com/uploaded_files/download/pranav-murthy-dheerender-thakur_fISZy.pdf

[32] Mehra, A. (2024, August 1). HYBRID AI MODELS: INTEGRATING SYMBOLIC REASONING WITH DEEP LEARNING FOR COMPLEX DECISION-MAKING. https://www.jetir.org/view?paper=JETIR2408685

[33] Kanungo, S., Kumar, A., & Zagade, R. (2022). OPTIMIZING ENERGY CONSUMPTION FOR IOT IN DISTRIBUTED COMPUTING. Journal of Emerging Technologies and Innovative Research, Volume 9(Issue 6). https://www.jetir.org/papers/JETIR2206A70.pdf

[34] Kanungo, S. (2024, April 16). Edge-to-Cloud Intelligence: Enhancing IoT Devices with Machine Learning and Cloud Computing - IRE Journals. IRE Journals. https://www.irejournals.com/index.php/paper-details/1701284

[35] Kanungo, S. (2020). Decoding AI: Transparent Models forUnderstandable Decision-Making. propulsiontechjournal.com. https://doi.org/10.52783/tjjpt.v41.i4.5637

[36] Nasr Esfahani, M. (2023). Breaking language barriers: How multilingualism can address gender disparities in US STEM fields. International Journal of All Research Education and Scientific Methods, 11(08), 2090-2100. https://doi.org/10.56025/IJARESM.2024.1108232090

[37] Favour: Hossain, M., & Madasani, R. C. (2023, October). Improving the Long-Term Durability of Polymers Used in Biomedical Applications. In ASME International Mechanical Engineering Congress and Exposition (Vol. 87615, p. V004T04A020). American Society of Mechanical Engineers.

[38] Madasani, R. C., & Reddy, K. M. (2014). Investigation Analysis on the performance improvement of a vapor compression refrigeration system. Applied Mechanics and Materials, 592, 1638-1641.

[39] Oyeniyi, J. Combating Fingerprint Spoofing Attacks through Photographic Sources.

[40] Bhadani, U. (2020). Hybrid Cloud: The New Generation of Indian Education Society.

[41] Bhadani, U. A Detailed Survey of Radio Frequency Identification (RFID) Technology: Current Trends and Future Directions.

[42] Bhadani, U. (2022). Comprehensive Survey of Threats, Cyberattacks, and Enhanced Countermeasures in RFID Technology. International Journal of Innovative Research in Science, Engineering and Technology, 11(2).

@article{1704087,
      author = {Kodamasimham Krishna, Aditya Mehra, Mithun Sarker, Lalit Mishra},
      title = {Cloud-Based Reinforcement Learning for Autonomous Systems: Implementing Generative AI for Real-time Decision Making and Adaptation},
      journal = {Iconic Research And Engineering Journals},
      year = {2023},
      volume = {6},
      number = {8},
      pages = {268-278},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/17040871.pdf},
      abstract = {The combination of Generative AI and Reinforcement Learning (RL) has made the realm of autonomous systems exciting because it considers complementary methods of enabling a real-time decision-making process. Generative AI is ideal for realistic data and simulation; however, RL defines how to learn actions in the environment by engaging in them. They let the autonomous systems predict further stances, manipulate complex conditions, and learn with the state of affairs in the environments always in place. This paper introduces the reader to Generative AI and RL and its application to autonomous systems. It will also discuss real-life examples of using Generative AI and RL in the automotive industry, industrial robots, medicine, energy, and logistics. The discussion also covers the potential changes in the technologies and how they may go further, including real-time learning at the edge, ethical interest controversies, and simulation or algorithmic computations improvements. The combination of Generative AI and Reinforcement Learning is a jump to the next level in achieving higher levels of self-organizing autonomously working systems, especially in areas of application where high levels of performance, flexibility, and robustness are requested.},
      keywords = {Generative AI, Reinforcement Learning (RL), Autonomous Systems, Real-time Adaptation, Predictive Modeling, Industrial Robotics, Autonomous Vehicles},
      month = {February},
  }