2 citations · 5 across the 18 of their papers we have counts for
4 papers · 1 filter
LEASE: Offline Preference-based Reinforcement Learning with High Sample Efficiency
Xiao-Yin Liu, Guotao Li, Xiao-Hu Zhou +1
Offline preference-based reinforcement learning (PbRL) provides an effective way to overcome the challenges of designing reward and the high costs of online interaction. However, s…
MICRO: Model-Based Offline Reinforcement Learning with a Conservative Bellman Operator
Xiao-Yin Liu, Xiao-Hu Zhou, Guotao Li +5
Offline reinforcement learning (RL) faces a significant challenge of distribution shift. Model-free offline RL penalizes the Q value for out-of-distribution (OOD) data or constrain…
CROP: Conservative Reward for Model-based Offline Policy Optimization
Hao Li, Xiao-Hu Zhou, Shu-Hai Li +6
Offline reinforcement learning (RL) aims to optimize a policy using collected data without online interactions. Model-based approaches are particularly appealing for addressing off…
DOMAIN: MilDly COnservative Model-BAsed OfflINe Reinforcement Learning
Xiao-Yin Liu, Xiao-Hu Zhou, Mei-Jiang Gui +7
Model-based reinforcement learning (RL), which learns an environment model from the offline dataset and generates more out-of-distribution model data, has become an effective appro…