New research examines adversarial attacks on State-Space Models for robust RL

By PulseAugur Editorial · [1 sources] · 2026-06-18 19:15

A new research paper explores how adversarial attacks can impact probabilistic State-Space Models (SSMs) used in reinforcement learning. The study analyzes how attackers can alter observations within likelihood constraints to influence the latent state and policy decisions. This research aims to develop more robust reinforcement learning systems, particularly for safety-critical applications like robotics where reliable operation under various adverse conditions is crucial. AI

IMPACT This research could lead to more resilient AI systems in critical domains like robotics by improving how reinforcement learning agents handle uncertain or manipulated sensor data.

RANK_REASON The cluster contains a single arXiv preprint detailing a new research paper. [lever_c_demoted from research: ic=1 ai=1.0]

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New research examines adversarial attacks on State-Space Models for robust RL

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arXiv stat.ML TIER_1 English(EN) · D. Ríos Insua · 2026-06-18 19:15

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

Decision-making under partial or adversarial observability requires accurate inference of the environment's latent state and its associated uncertainty. This work analyses adversarial attacks on linear probabilistic state-space models, commonly integrated within reinforcement lea…

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

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