The management of heavy traffic demands has been significantly improved by employing synchronized traffic signal control at multiple intersections. Multi-agent Reinforcement Learning (MARL) techniques have been widely utilized to achieve this coordination. However, these approaches predominantly depend on manually crafted features from adjacent intersections, which impedes their generalization to new scenarios. Furthermore, while displaying high accuracy for specific traffic flow patterns, these methods often lack the necessary robustness for other patterns. In this study, our objective is to develop an effective signal timing plan by directly learning the minimal required communication between intersections from traffic data. We introduce a novel, comprehensive approach that combines multi-agent reinforcement learning with a learned communication mechanism. Our model incorporates a coordinated actor network and a centralized critic network to address the challenges of non-stationarity. We conducted extensive experiments comparing our model with other commonly used non-RL and benchmark MARL techniques. The evaluation results show that our proposed model, which relies only on local sensory input and a single message from neighboring intersections, excels in managing various traffic flow patterns. Furthermore, our model outperforms competing approaches in terms of robustness, resilience, and overall performance.

Efforts to predict stock market outcomes have yielded limited success due to the inherently stochastic nature of the market, influenced by numerous unpredictable factors. Many existing prediction approaches focus on single-point predictions, lacking the depth needed for effective decision-making and often overlooking market risk. To bridge this gap, we proposeRAGIC, a novel risk-aware framework for stockintervalprediction to quantify uncertainty. Our approach leverages a Generative Adversarial Network (GAN) to produce future price sequences infused with randomness inherent in financial markets.RAGIC’s generator detects the risk perception of informed investors and captures historical price trends globally and locally. Then therisk-sensitive intervalsis built upon the simulated future prices from sequence generation through statistical inference, incorporatinghorizon-wiseinsights. The interval’s width is adaptively adjusted to reflect market volatility. Importantly, our approach relies solely on publicly available data and incurs only low computational overhead.RAGIC’s evaluation across globally recognized broad-based indices demonstrates its balanced performance, offering both accuracy and informativeness. Achieving a consistent 95% coverage,RAGICmaintains a narrow interval width. This promising outcome suggests that our approach effectively addresses the challenges of stock market prediction while incorporating vital risk considerations.

Traffic signal control is safety-critical for our daily life. Roughly one-quarter of road accidents in the U.S. happen at intersections due to problematic signal timing, urging the development of safety-oriented intersection control. However, existing studies on adaptive traffic signal control using reinforcement learning technologies have focused mainly on minimizing traffic delay but neglecting the potential exposure to unsafe conditions. We, for the first time, incorporate road safety standards as enforcement to ensure the safety of existing reinforcement learning methods, aiming toward operating intersections with zero collisions. We have proposed a safety-enhanced residual reinforcement learning method (SafeLight) and employed multiple optimization techniques, such as multi-objective loss function and reward shaping for better knowledge integration. Extensive experiments are conducted using both synthetic and real-world benchmark datasets. Results show that our method can significantly reduce collisions while increasing traffic mobility.