This study proposes a novel framework, Mamba-DQN, which integrates the state space-based time-series encoder Mamba-SSM into the Deep Q-Network (DQN) architecture to improve reinforcement learning performance in dynamic environments. Conventional reinforcement learning models primarily rely on instantaneous state information, limiting their ability to effectively capture temporal dependencies. To address this limitation, the proposed Mamba-DQN generates latent representations that summarize temporal information from state sequences and utilizes them for both Q-value estimation and Prioritized Experience Replay (PER), thereby enhancing the adaptability of policy learning and improving sample efficiency. The Mamba-SSM offers linear computational complexity and is optimized for parallel processing, enabling real-time learning and policy updates even in environments characterized by high state transition rates. The effectiveness of the proposed framework was validated through experiments conducted in environments with strong temporal dependencies and sparse rewards. Experimental results demonstrate that Mamba-DQN achieves superior stability and efficiency in policy learning compared to conventional DQN, LSTM-DQN, and Transformer-DQN models.

Stereo matching yields metrically grounded disparity but often fails in textureless, repetitive, reflective, or occluded regions, while monocular depth estimation provides dense structure yet suffers from scale ambiguity and weaker local geometric fidelity. We present CR-FusionNet, a lightweight stereo– monocular fusion framework designed for resource-constrained robotic perception. Our pipeline uses CPU-based Semi-Global Block Matching (SGBM) as a deterministic metric anchor and fuses it with a monocular prior through a compact fully convolutional encoder–decoder. Before fusion, we align the monocular prediction to the stereo disparity scale via robust affine regression on valid stereo pixels. CR-FusionNet then performs confidence-aware routing between stereo and monocular cues and applies residual refinement to mitigate SGBM discretization artifacts, producing dense and stable metric depth. Experiments on KITTI Stereo 2015, Middlebury, and TartanAir show consistent improvements over single-source baselines while requiring significantly less GPU memory than deep stereo networks, supporting efficient onboard deployment.