LP-DS improves generative policies for imitation and RL by optimizing latent noise perturbations with a constrained Lagrangian objective, showing up to 25% better returns on manipulation and locomotion tasks.
Space odyssey: An experimental software security analysis of satellites
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
Adapts PACE methodology to satellites with a graph-based state-transition model and dynamic resilience index, evaluating static, adaptive, and epsilon-greedy variants for improved survivability.
citing papers explorer
-
Lagrangian Perturbation Diffusion Steering: Latent Reinforcement Learning for Generative Policies
LP-DS improves generative policies for imitation and RL by optimizing latent noise perturbations with a constrained Lagrangian objective, showing up to 25% better returns on manipulation and locomotion tasks.
-
Resilience Through Escalation: A Graph-Based PACE Architecture for Satellite Threat Response
Adapts PACE methodology to satellites with a graph-based state-transition model and dynamic resilience index, evaluating static, adaptive, and epsilon-greedy variants for improved survivability.