Multivariate time series forecasting (MTSF) aims to predict future values based on historical observations. Recently, Fuzzy Cognitive Maps (FCM) have emerged as a promising and interpretable approach for MTSF. However, existing FCM-based models suffer from two main limitations. First, their feature extraction mechanisms fail to effectively represent raw time series data, limiting the ability to capture complex spatiotemporal dependencies. Second, the single-variable composite modeling strategy adopted by high-order FCMs (HFCM) neglects holistic inter-variable relationships across time, leading to inefficiencies and a linear increase in model parameters. To overcome these challenges, we propose a novel framework—Context-Aware Enhanced FCM (CAE-FCM)—for interpretable MTSF. To address the first limitation, CAE-FCM introduces two complementary feature extraction modules: the Adaptive Graph Convolution (AGC) module, which captures spatial dependencies through neighborhood-aware information aggregation, and the Global-Local Context-Aware Mamba (GLCAM) module, which models temporal dependencies via a state space model (SSM) that learns global and local temporal dynamics. For second limitation, CAE-FCM integrates the extracted spatial and temporal features into unified high-dimensional representations for FCM nodes, enabling efficient and expressive modeling of complex spatiotemporal interactions. Extensive experiments on five benchmark datasets demonstrate that CAE-FCM achieves state-of-the-art performance, significantly outperforming HFCM-based baselines in both forecasting accuracy and computational efficiency.

Motivated by the first priority of safety in many real-world applications, we propose MaxSafe, a chance-constrained bi-level optimization framework for safe reinforcement learning. MaxSafe first minimizes the unsafe probability and then maximizes the return among the safest policies. We provide a tailored Q-learning algorithm for the MaxSafe objective, featuring a novel learning process for optimal action masks with theoretical convergence guarantees. To enable the application of our algorithm to large-scale experiments, we introduce two key techniques: safety polarization and safety prioritized experience replay. Safety polarization generalizes the optimal action masking by polarizing the Q-function, which assigns low values to unsafe state-action pairs, effectively discouraging their selection. In parallel, safety prioritized experience replay enhances the learning of optimal action masks by prioritizing samples based on temporal-difference (TD) errors derived from our proposed state-action reachability estimation functions. This approach efficiently addresses the challenges posed by sparse cost signals. Experiments on diverse autonomous driving and safe control tasks show that our methods achieve near-maximal safety and an optimal reward-safety trade-off.

In this article, a reinforcement learning (RL)-based strategy for unmanned surface vehicle (USV) path following control is developed. The proposed method learns integrated guidance and heading control policy, which directly maps the USV's navigation states to motor control commands. By introducing a twin-critic design and an integral compensator to the conventional deep deterministic policy gradient (DDPG) algorithm, the tracking accuracy and robustness of the controller can be significantly improved. Moreover, a pretrained neural network-based USV model is built to help the learning algorithm efficiently deal with unknown nonlinear dynamics. The self-learning and path following capabilities of the proposed method were validated in both simulations and real sea experiments. The results show that our control policy can achieve better performance than a traditional cascade control policy and a DDPG-based control policy.