1 citations · 2 across the 15 of their papers we have counts for
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Improving Sample Efficiency of Reinforcement Learning with Background Knowledge from Large Language Models
Fuxiang Zhang, Junyou Li, Yi-Chen Li +3
Low sample efficiency is an enduring challenge of reinforcement learning (RL). With the advent of versatile large language models (LLMs), recent works impart common-sense knowledge…
Q-Adapter: Customizing Pre-trained LLMs to New Preferences with Forgetting Mitigation
Yi-Chen Li, Fuxiang Zhang, Wenjie Qiu +5
Large Language Models (LLMs), trained on a large amount of corpus, have demonstrated remarkable abilities. However, it may not be sufficient to directly apply open-source LLMs like…
BWArea Model: Learning World Model, Inverse Dynamics, and Policy for Controllable Language Generation
Chengxing Jia, Pengyuan Wang, Ziniu Li +4
Large language models (LLMs) have catalyzed a paradigm shift in natural language processing, yet their limited controllability poses a significant challenge for downstream applicat…
Any-step Dynamics Model Improves Future Predictions for Online and Offline Reinforcement Learning
Haoxin Lin, Yu-Yan Xu, Yihao Sun +6
Model-based methods in reinforcement learning offer a promising approach to enhance data efficiency by facilitating policy exploration within a dynamics model. However, accurately…
Disentangling Policy from Offline Task Representation Learning via Adversarial Data Augmentation
Chengxing Jia, Fuxiang Zhang, Yi-Chen Li +5
Offline meta-reinforcement learning (OMRL) proficiently allows an agent to tackle novel tasks while solely relying on a static dataset. For precise and efficient task identificatio…
Debiased Offline Representation Learning for Fast Online Adaptation in Non-stationary Dynamics
Xinyu Zhang, Wenjie Qiu, Yi-Chen Li +4
Developing policies that can adjust to non-stationary environments is essential for real-world reinforcement learning applications. However, learning such adaptable policies in off…