Operator-Guided Invariance Learning for Continuous Reinforcement Learning
概要
arXiv:2605.06500v1 Announce Type: cross Abstract: Reinforcement learning (RL) with continuous time and state/action spaces is often data-intensive and brittle under nuisance variability and shift, motivating methods that exploit value-preserving structures to stabilize and improve learning. Most ex…
