The diagnosis of cerebral diseases often requires imaging to assess the state of the brain. Magnetic resonance imaging (MRI) is routinely used to detect brain disorders. However, the obtained MR images often lack sufficient resolution due to restrictions such as patient comfort and prolonged sampling period. Analyzing the low-resolution MRI may result in an erroneous diagnosis. Consequently, there is a need for super-resolution methods to acquire high-resolution MRI images. The Reconstruction-based Super Resolution (SR) approach is a kind of single image SR that reconstructs low-resolution MRI scans into high-resolution pictures. The proposed Vit+TransGAN+RL integrates Vision Transformer (ViT), Transformerbased Generative Adversarial Network (Trans GAN), and Reinforcement Learning (RL), wherein the RL agent dynamically regulates the k-space sampling and reconstruction process by selecting optimal acquisition or refinement actions based on a learned policy that maximizes image quality while minimizing acquisition time. Experimental evaluation demonstrates that the proposed ViT+TransGAN + RL framework achieves 11.47% of MSE, 40.12% of PSNR, 0.7792% of SSIM, 0.1665 of FSIM, -1.4543 of NRMSE, 0.9183 of VIF, 0.8858 of DSC for degradation and 24.65% of MSE, 34.65% of MSE, 0.25 of SSIM, 0.094 of FSIM, -1.45 of NRMSE, 0.445 of VIF and 0.68 of DSC.