Beyond Traditional Algorithms: Harnessing Reinforcement Learning and Generative AI for Next-Generation Autonomous Systems

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@article{1701573,
      author = {Souratn Jain},
      title = {Beyond Traditional Algorithms: Harnessing Reinforcement Learning and Generative AI for Next-Generation Autonomous Systems},
      journal = {Iconic Research And Engineering Journals},
      year = {2019},
      volume = {3},
      number = {2},
      pages = {729-740},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1701573.pdf},
      abstract = {Combining RL with generative AI systems is a move towards developing enhanced generations of self-controlled robotic systems. In RL, computers learn from experience and feedback, while generative AI improves perception through realistic environment recreation, outcome predictions, and data setup. These technologies solve society's acute problems in unpredictable scenarios and develop various fields, including agriculture, construction, defense, oil and gas, and environmental management. This article elaborated on the joint work mode between RL and generative AI, their application in certain industries, and issues such as computational complexity, risk control, and ethical concerns. Similarly, it defines prospects, such as utilizing efficient algorithms for multi-agent systems and human-AI interfaces to underscore the capabilities of redefining autonomous systems. When combined correctly, RL and generative AI create new opportunities for effective and creative application of AI solutions to address numerous challenges today.},
      keywords = {Reinforcement learning, generative AI, autonomous systems, synthetic environments, predictive modeling, machine learning},
      month = {August},
  }