4 papers
DRL-STAF: A Deep Reinforcement Learning Framework for State-Aware Forecasting of Complex Multivariate Hidden Markov Processes
Manrui Jiang, Jingru Huang, Yong Chen +1
Forecasting multivariate hidden Markov processes is challenging due to nonlinear and nonstationary observations, latent state transitions, and cross-sequence dependencies. While de…
Planning-Augmented Sampling with Early Guidance for High-Reward Discovery
Rui Zhu, Yudong Zhang, Xuan Yu +3
Generative Flow Networks (GFlowNets) enable structured generation with inherent diversity, but existing sampling strategies often rely on weak guided exploration, slowing early dis…
CroTad: A Contrastive Reinforcement Learning Framework for Online Trajectory Anomaly Detection
Rui Xue, Dan He, Fengmei Jin +2
Detecting trajectory anomalies is a vital task in modern Intelligent Transportation Systems (ITS), enabling the identification of unsafe, inefficient, or irregular travel behaviour…
From Theory to Practice with RAVEN-UCB: Addressing Non-Stationarity in Multi-Armed Bandits through Variance Adaptation
Junyi Fang, Yuxun Chen, Yuxin Chen +1
The Multi-Armed Bandit (MAB) problem is challenging in non-stationary environments where reward distributions evolve dynamically. We introduce RAVEN-UCB, a novel algorithm that com…