Label-flip attacks on log-linear DPO reduce to binary sparse approximation problems that can be solved efficiently by lattice-based and binary matching pursuit methods with recovery guarantees.
arXiv preprint arXiv:1712.03632 , year=
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
abstract
This paper proposes adversarial attacks for Reinforcement Learning (RL) and then improves the robustness of Deep Reinforcement Learning algorithms (DRL) to parameter uncertainties with the help of these attacks. We show that even a naively engineered attack successfully degrades the performance of DRL algorithm. We further improve the attack using gradient information of an engineered loss function which leads to further degradation in performance. These attacks are then leveraged during training to improve the robustness of RL within robust control framework. We show that this adversarial training of DRL algorithms like Deep Double Q learning and Deep Deterministic Policy Gradients leads to significant increase in robustness to parameter variations for RL benchmarks such as Cart-pole, Mountain Car, Hopper and Half Cheetah environment.
fields
cs.LG 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
The IBAL framework builds information-theoretic attacks that break agent interactions in MARL and trains policies to stay robust under observation and action perturbations.
citing papers explorer
-
Efficient Preference Poisoning Attack on Offline RLHF
Label-flip attacks on log-linear DPO reduce to binary sparse approximation problems that can be solved efficiently by lattice-based and binary matching pursuit methods with recovery guarantees.
-
Interaction-Breaking Adversarial Learning Framework for Robust Multi-Agent Reinforcement Learning
The IBAL framework builds information-theoretic attacks that break agent interactions in MARL and trains policies to stay robust under observation and action perturbations.