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@article{189817,
        author = {K Kruthi and Krishna S Bhat and Rashmitha N and Tejaswini Sharanappa Patil and Priyanka M.E},
        title = {Resource Allocation using Reinforcement Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {8},
        pages = {1491-1497},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=189817},
        abstract = {Resource allocation is a critical challenge in modern computing environments where limited system resources must be shared among multiple competing tasks. Traditional static allocation methods fail to adapt to dynamic workloads and varying demand patterns. This paper presents an intelligent resource allocation system inspired by Reinforcement Learning (RL) principles for efficient distribution of system resources. The proposed system analyzes multimedia inputs such as documents, images, audio, and video files and allocates CPU, memory, and bandwidth based on file size, type, and processing complexity. The allocation logic ensures fairness, minimizes resource wastage, and improves overall system efficiency. Experimental results demonstrate effective resource utilization and adaptability, making the system suitable for real-world applications such as cloud computing and multimedia processing platforms.},
        keywords = {Reinforcement Learning, Resource Allocation, CPU Scheduling, Bandwidth Allocation, Multimedia Processing.},
        month = {January},
        }