2 citations · 4 across the 10 of their papers we have counts for
5 papers · 1 filter
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…
Predictive Coding for Decision Transformer
Tung M. Luu, Donghoon Lee, Chang D. Yoo
Recent work in offline reinforcement learning (RL) has demonstrated the effectiveness of formulating decision-making as return-conditioned supervised learning. Notably, the decisio…
MDSGen: Fast and Efficient Masked Diffusion Temporal-Aware Transformers for Open-Domain Sound Generation
Trung X. Pham, Tri Ton, Chang D. Yoo
We introduce MDSGen, a novel framework for vision-guided open-domain sound generation optimized for model parameter size, memory consumption, and inference speed. This framework in…
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…
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…