15 citations · 19 across the 12 of their papers we have counts for
12 papers · 1 filter
Fast and Highly Expressive Policy Learning for Offline Reinforcement Learning via Bootstrapped Flow Q-Learning
Thanh Nguyen, Tri Ton, Hongbin Choe +2
Diffusion-based Q-learning has emerged as a powerful paradigm for offline reinforcement learning, but its reliance on multi-step denoising makes both training and inference computa…
Uncertainty-Aware Rank-One MIMO Q Network Framework for Accelerated Offline Reinforcement Learning
Thanh Nguyen, Tung Luu, Tri Ton +2
Offline reinforcement learning (RL) has garnered significant interest due to its safe and easily scalable paradigm. However, training under this paradigm presents its own challenge…
Policy Learning from Large Vision-Language Model Feedback without Reward Modeling
Tung M. Luu, Donghoon Lee, Younghwan Lee +1
Offline reinforcement learning (RL) provides a powerful framework for training robotic agents using pre-collected, suboptimal datasets, eliminating the need for costly, time-consum…
Enhancing Rating-Based Reinforcement Learning to Effectively Leverage Feedback from Large Vision-Language Models
Tung Minh Luu, Younghwan Lee, Donghoon Lee +3
Designing effective reward functions remains a fundamental challenge in reinforcement learning (RL), as it often requires extensive human effort and domain expertise. While RL from…
Sample Efficient Reinforcement Learning via Large Vision Language Model Distillation
Donghoon Lee, Tung M. Luu, Younghwan Lee +1
Recent research highlights the potential of multimodal foundation models in tackling complex decision-making challenges. However, their large parameters make real-world deployment…
Reward Generation via Large Vision-Language Model in Offline Reinforcement Learning
Younghwan Lee, Tung M. Luu, Donghoon Lee +1
In offline reinforcement learning (RL), learning from fixed datasets presents a promising solution for domains where real-time interaction with the environment is expensive or risk…