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cs.LG2024
CUQDS: Conformal Uncertainty Quantification under Distribution Shift for Trajectory Prediction
Huiqun Huang, Sihong He, Fei Miao
Trajectory prediction models that can infer both finite future trajectories and their associated uncertainties of the target vehicles in an online setting (e.g., real-world applica…
cs.LG2024★ 1 cited
Momentum for the Win: Collaborative Federated Reinforcement Learning across Heterogeneous Environments
Han Wang, Sihong He, Zhili Zhang +2
We explore a Federated Reinforcement Learning (FRL) problem where agents collaboratively learn a common policy without sharing their trajectory data. To date, existing FRL work…
cs.LG2024
Constrained Reinforcement Learning Under Model Mismatch
Zhongchang Sun, Sihong He, Fei Miao +1
Existing studies on constrained reinforcement learning (RL) may obtain a well-performing policy in the training environment. However, when deployed in a real environment, it may ea…