5 citations · 6 across the 5 of their papers we have counts for
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cs.LG2023★ 5 cited
PROTO: Iterative Policy Regularized Offline-to-Online Reinforcement Learning
Jianxiong Li, Xiao Hu, Haoran Xu +3
Offline-to-online reinforcement learning (RL), by combining the benefits of offline pretraining and online finetuning, promises enhanced sample efficiency and policy performance. H…
cs.LG2023
Query-Policy Misalignment in Preference-Based Reinforcement Learning
Xiao Hu, Jianxiong Li, Xianyuan Zhan +2
Preference-based reinforcement learning (PbRL) provides a natural way to align RL agents' behavior with human desired outcomes, but is often restrained by costly human feedback. To…
cs.LG2023★ 1 cited
Mind the Gap: Offline Policy Optimization for Imperfect Rewards
Jianxiong Li, Xiao Hu, Haoran Xu +4
Reward function is essential in reinforcement learning (RL), serving as the guiding signal to incentivize agents to solve given tasks, however, is also notoriously difficult to des…