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20242026
most citedTowards Robust Policy: Enhancing Offline Reinforcement Learning with Adversarial Attacks and Defenses

1 citations · 2 across the 7 of their papers we have counts for

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cs.LG2026

Compositional Average Velocity Modeling for Efficient Diffusion Offline Reinforcement Learning

Thanh Nguyen, Abu Hanif Muhammad Syarubany, Hwanhee Kim +1

Diffusion-based policies have recently emerged as a powerful paradigm for offline reinforcement learning, but their reliance on multi-step denoising introduces substantial computat…

cs.LG2026

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…

cs.LG2026★ 1 cited

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…

cs.LG2025

One-Step Flow Q-Learning: Addressing the Diffusion Policy Bottleneck in Offline Reinforcement Learning

Thanh Nguyen, Chang D. Yoo

Diffusion Q-Learning (DQL) has established diffusion policies as a high-performing paradigm for offline reinforcement learning, but its reliance on multi-step denoising for action…

cs.LG2024

Mitigating Adversarial Perturbations for Deep Reinforcement Learning via Vector Quantization

Tung M. Luu, Thanh Nguyen, Tee Joshua Tian Jin +2

Recent studies reveal that well-performing reinforcement learning (RL) agents in training often lack resilience against adversarial perturbations during deployment. This highlights…

cs.LG2024

On the Perturbed States for Transformed Input-robust Reinforcement Learning

Tung M. Luu, Haeyong Kang, Tri Ton +2

Reinforcement Learning (RL) agents demonstrating proficiency in a training environment exhibit vulnerability to adversarial perturbations in input observations during deployment. T…

cs.LG2024★ 1 cited

Towards Robust Policy: Enhancing Offline Reinforcement Learning with Adversarial Attacks and Defenses

Thanh Nguyen, Tung M. Luu, Tri Ton +1

Offline reinforcement learning (RL) addresses the challenge of expensive and high-risk data exploration inherent in RL by pre-training policies on vast amounts of offline data, ena…