13 citations · 16 across the 10 of their papers we have counts for
5 papers · 1 filter
Multiple Weaks Win Single Strong: Large Language Models Ensemble Weak Reinforcement Learning Agents into a Supreme One
Yiwen Song, Qianyue Hao, Qingmin Liao +2
Model ensemble is a useful approach in reinforcement learning (RL) for training effective agents. Despite wide success of RL, training effective agents remains difficult due to the…
LLM-Explorer: A Plug-in Reinforcement Learning Policy Exploration Enhancement Driven by Large Language Models
Qianyue Hao, Yiwen Song, Qingmin Liao +2
Policy exploration is critical in reinforcement learning (RL), where existing approaches include greedy, Gaussian process, etc. However, these approaches utilize preset stochastic…
DeepSTA: A Spatial-Temporal Attention Network for Logistics Delivery Timely Rate Prediction in Anomaly Conditions
Jinhui Yi, Huan Yan, Haotian Wang +2
Prediction of couriers' delivery timely rates in advance is essential to the logistics industry, enabling companies to take preemptive measures to ensure the normal operation of de…
Learning to Estimate Package Delivery Time in Mixed Imbalanced Delivery and Pickup Logistics Services
Jinhui Yi, Huan Yan, Haotian Wang +2
Accurately estimating package delivery time is essential to the logistics industry, which enables reasonable work allocation and on-time service guarantee. This becomes even more n…
Noise Matters: Diffusion Model-based Urban Mobility Generation with Collaborative Noise Priors
Yuheng Zhang, Yuan Yuan, Jingtao Ding +2
With global urbanization, the focus on sustainable cities has largely grown, driving research into equity, resilience, and urban planning, which often relies on mobility data. The…