Safe Continual RL Framework Maintains Safety Constraints While Adapting to Non-Stationary Environments

arXiv·medium signalXBlueskyLinkedInCopy link

Coursey, Diaz-Gonzalez, and Quinones-Grueiro tackle a critical gap: most RL methods assume stationarity, but real deployments face distributional shift over time. Their framework maintains safety constraints during continual adaptation, preventing the catastrophic safety violations that occur when a policy trained on one distribution encounters a shifted environment. Relevant for anyone deploying RL controllers in production where conditions change (robotics, resource management, adaptive systems).