44 citations · 71 across the 10 of their papers we have counts for
7 papers · 1 filter
OLLIE: Imitation Learning from Offline Pretraining to Online Finetuning
Sheng Yue, Xingyuan Hua, Ju Ren +3
In this paper, we study offline-to-online Imitation Learning (IL) that pretrains an imitation policy from static demonstration data, followed by fast finetuning with minimal enviro…
How to Leverage Diverse Demonstrations in Offline Imitation Learning
Sheng Yue, Jiani Liu, Xingyuan Hua +4
Offline Imitation Learning (IL) with imperfect demonstrations has garnered increasing attention owing to the scarcity of expert data in many real-world domains. A fundamental probl…
Federated Offline Policy Optimization with Dual Regularization
Sheng Yue, Zerui Qin, Xingyuan Hua +2
Federated Reinforcement Learning (FRL) has been deemed as a promising solution for intelligent decision-making in the era of Artificial Internet of Things. However, existing FRL ap…
Momentum-Based Federated Reinforcement Learning with Interaction and Communication Efficiency
Sheng Yue, Xingyuan Hua, Lili Chen +1
Federated Reinforcement Learning (FRL) has garnered increasing attention recently. However, due to the intrinsic spatio-temporal non-stationarity of data distributions, the current…
CLARE: Conservative Model-Based Reward Learning for Offline Inverse Reinforcement Learning
Sheng Yue, Guanbo Wang, Wei Shao +4
This work aims to tackle a major challenge in offline Inverse Reinforcement Learning (IRL), namely the reward extrapolation error, where the learned reward function may fail to exp…
Efficient Federated Meta-Learning over Multi-Access Wireless Networks
Sheng Yue, Ju Ren, Jiang Xin +3
Federated meta-learning (FML) has emerged as a promising paradigm to cope with the data limitation and heterogeneity challenges in today's edge learning arena. However, its perform…
Inexact-ADMM Based Federated Meta-Learning for Fast and Continual Edge Learning
Sheng Yue, Ju Ren, Jiang Xin +2
In order to meet the requirements for performance, safety, and latency in many IoT applications, intelligent decisions must be made right here right now at the network edge. Howeve…