← Back to all news
arXiv Machine Learning By M. Santos-Pascual, D. R\'ios Insua

Adversarial observations in probabilistic State-Space Models for robust Reinforcement Learning

Read the original on arXiv Machine Learning →

arXiv:2606. 20880v2 Announce Type: replace-cross Abstract: Decision-making under partial or adversarial observability requires accurate inference of the environment's latent state and its associated uncertainty.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv AI
Jun 10

Robust Deep Reinforcement Learning Through Adversarial Attacks and Training : A Survey

arXiv:2403. 00420v3 Announce Type: replace-cross Abstract: Deep Reinforcement Learning (DRL) is a subfield of machine learning for training autonomous agents that take sequential actions across complex environments.

By Lucas Schott, Josephine Delas, Hatem Hajri, Elies Gherbi, Reda Yaich, Nora Boulahia-Cuppens, Frederic Cuppens, Sylvain Lamprier
agentsreinforcement-learningsafety
More like this →
arXiv AI
Aug 25

Robust Multi-Agent Reinforcement Learning for Small UAS Separation Assurance under GPS Degradation and Spoofing

The paper presents a robust multi‑agent reinforcement learning framework for small unmanned aircraft systems (sUAS) to maintain separation assurance when GPS data is degraded or spoofed. By modeling state observation corruption as a zero‑sum game, the authors derive a closed‑form adversarial perturbation that eliminates iterative inner optimization and can be evaluated in linear time. Integrating this perturbation into a policy‑gradient MARL algorithm yields a counter‑policy that achieves near‑zero collision rates in high‑density simulations even with up to 35% observation corruption, outperforming non‑adversarial baselines.

By Alex Zongo, Filippos Fotiadis, Ufuk Topcu, Peng Wei
agentsreinforcement-learningsafety
More like this →