5 citations · 12 across the 9 of their papers we have counts for
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cs.LG2023★ 1 cited
Augmenting Unsupervised Reinforcement Learning with Self-Reference
Andrew Zhao, Erle Zhu, Rui Lu +3
Humans possess the ability to draw on past experiences explicitly when learning new tasks and applying them accordingly. We believe this capacity for self-referencing is especially…
cs.LG2023★ 1 cited
Understanding, Predicting and Better Resolving Q-Value Divergence in Offline-RL
Yang Yue, Rui Lu, Bingyi Kang +2
The divergence of the Q-value estimation has been a prominent issue in offline RL, where the agent has no access to real dynamics. Traditional beliefs attribute this instability to…
cs.LG2023★ 1 cited
Train Once, Get a Family: State-Adaptive Balances for Offline-to-Online Reinforcement Learning
Shenzhi Wang, Qisen Yang, Jiawei Gao +6
Offline-to-online reinforcement learning (RL) is a training paradigm that combines pre-training on a pre-collected dataset with fine-tuning in an online environment. However, the i…