most citedDiverse Randomized Value Functions: A Provably Pessimistic Approach for Offline Reinforcement Learning

1 citations · 1 across the 1 of their papers we have counts for

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5 papers

cs.LG2024

Ensemble Successor Representations for Task Generalization in Offline-to-Online Reinforcement Learning

Changhong Wang, Xudong Yu, Chenjia Bai +2

In Reinforcement Learning (RL), training a policy from scratch with online experiences can be inefficient because of the difficulties in exploration. Recently, offline RL provides…

cs.LG2024

Contrastive Representation for Data Filtering in Cross-Domain Offline Reinforcement Learning

Xiaoyu Wen, Chenjia Bai, Kang Xu +4

Cross-domain offline reinforcement learning leverages source domain data with diverse transition dynamics to alleviate the data requirement for the target domain. However, simply m…

cs.LG20241 cited

Diverse Randomized Value Functions: A Provably Pessimistic Approach for Offline Reinforcement Learning

Xudong Yu, Chenjia Bai, Hongyi Guo +2

Offline Reinforcement Learning (RL) faces distributional shift and unreliable value estimation, especially for out-of-distribution (OOD) actions. To address this, existing uncertai…

cs.LG2024

Regularized Conditional Diffusion Model for Multi-Task Preference Alignment

Xudong Yu, Chenjia Bai, Haoran He +2

Sequential decision-making is desired to align with human intents and exhibit versatility across various tasks. Previous methods formulate it as a conditional generation process, u…

cs.LG2023

Towards Robust Offline-to-Online Reinforcement Learning via Uncertainty and Smoothness

Xiaoyu Wen, Xudong Yu, Rui Yang +3

To obtain a near-optimal policy with fewer interactions in Reinforcement Learning (RL), a promising approach involves the combination of offline RL, which enhances sample efficienc…