Low-dose computed tomography （LDCT） has become an important imaging modality for reducing patient radiation exposure in diagnostic and screening protocols. However，the inherent trade-off between radiation dose and image fidelity introduces severe quantum noise and streak artifacts，which degrade anatomical visibility and reduce diagnostic confidence. Although deep-learning-based reconstruction methods，especially convolutional neural networks （CNNs） and generative adversarial networks （GANs），have made remarkable progress in learning direct mappings from LDCT to normal-dose CT （NDCT），they still suffer from several intrinsic limitations. These include a tendency toward excessive smoothing that erodes fine pathological detail，sensitivity to distribution shifts between training and clinical data，high computational complexity，and a static processing paradigm that cannot adapt to the spatially heterogeneous noise characteristics typical of LDCT scans. To address these challenges，this paper introduces HRDRL-Net （high-resolution deep reinforcement learning network），a novel framework that reformulates LDCT reconstruction as an adaptive，pixel-level decision-making process. The goal is to achieve an optimal，context-aware balance between aggressive noise suppression and faithful preservation of diagnostically important structures.

HRDRL-Net is grounded in the theory of Markov decision processes （MDPs） and implemented using the asynchronous advantage actor-critic （A3C） algorithm. Its core innovation is a dual-path，multi-branch cooperative architecture. The upper path contains a dedicated low-dose noise suppression module （LDNM） with four branches—dilated convolution，identity mapping，detail enhancement，and noise suppression—combined with a dual-attention mechanism that includes both channel and spatial attention for multi-scale semantic feature extraction and refinement. The lower path uses a predefined Sobel operator followed by convolutional layers to explicitly extract and enhance structural edge information. Features from the two paths are fused adaptively through a gating mechanism，producing a rich state representation for the agent. A comprehensive action space of 14 predefined image-processing operators is further designed，including Gaussian filters of different strengths，edge-preserving bilateral filters，median filtering，non-local means filtering，guided filtering，and adaptive sharpening. This allows the agent to select the most appropriate local processing strategy for each region. Training is guided by a composite reward function with three components： 1） a pixel-level fidelity term that minimizes mean squared error，2） a gradient-structure similarity term that enforces edge and texture consistency，and 3） a local variance penalty term that selectively suppresses noise in homogeneous regions without over-smoothing high-gradient anatomical details. Both the policy network （actor） and the value network （critic） are implemented as fully convolutional networks，enabling efficient per-pixel action-probability prediction and state-value estimation. The entire system is trained end to end： the agent iteratively interacts with the LDCT image environment，receives rewards based on its reconstruction actions，and updates its policy to maximize cumulative future quality gains.

HRDRL-Net was rigorously evaluated on two publicly available benchmark datasets，the Mayo Clinic LDCT Challenge Dataset and the Piglet Dataset. It was compared with five state-of-the-art deep learning methods： residual encoder-decoder convolutional neural network （RED-CNN），dual domain generative adversarial network，DU-GAN），CT-former，self-adaptive weight embedded lightweight network （SWELNet），and multi-stage multi-order context aggregation （MMCA）. Quantitative results on the Mayo testset show that HRDRL-Net achieved a peak signal-to-noise ratio （PSNR） of 32.319 ± 1.016 dB，a structural similarity index measure （SSIM） of 0.897 ± 0.020，and a gradient magnitude similarity deviation （GMSD） of 0.063 ± 0.008，outperforming all competing methods. Compared with the baselines，this corresponds to an average improvement of ～1.3% in PSNR，～0.7% in SSIM，and a ～6% reduction in GMSD. On the Piglet dataset，the model achieved a PSNR of 32.263 ± 1.158 dB and an SSIM of 0.900 ± 0.009，confirming strong cross-domain generalization. Statistical significance was verified by Friedman tests （p < 0.001 for all metrics on both datasets） followed by post hoc Nemenyi tests. Ablation studies further validated each core design component： the composite reward improved PSNR by ~0.7 dB compared with a pixel-only reward； the full action set yielded an approximately 1.2% SSIM gain over a basic action set； the dual-path architecture reduced GMSD by ～2.3% relative to single-path variants； and the complete LDNM improved PSNR by 0.8 dB over a simple dilated-convolution baseline. Visual analysis，supported by residual maps and edge-intensity profiles，shows that HRDRL-Net achieves thorough noise removal in homogeneous regions while preserving fine vasculature，organ boundaries，and tissue textures，whereas competing methods tend to leave residual noise，blur structures，or distort anatomy. In addition，HRDRL-Net remains computationally practical，requiring only 0.3 GB of GPU memory and offering competitive inference speed，thereby balancing reconstruction quality and resource efficiency.

This work presents HRDRL-Net as a deep reinforcement learning framework that addresses the fundamental tension between noise suppression and detail preservation in LDCT reconstruction. By formulating reconstruction as a sequential，adaptive decision-making problem，HRDRL-Net moves beyond static processing pipelines. Its combination of a dual-path feature extractor，a rich action space，and a composite reward function enables intelligent pixel-level adaptation to local image content. Extensive experiments demonstrate its superiority over current methods in quantitative metrics，visual quality，statistical significance，and generalization，while maintaining practical efficiency. The framework therefore provides a robust，high-fidelity，and clinically promising solution，and it points to a new direction for intelligent，adaptive processing in medical imaging.