A Simple Unified Uncertainty-Guided Framework for Offline-to-Online Reinforcement Learning

This paper presents SUNG, a framework for offline-to-online reinforcement learning (RL), which is training an AI agent first on existing data and then improving it through live interactions. The framework addresses two main problems: limited exploration due to offline data constraints and distribution shift (when the agent encounters data patterns it wasn't trained on). SUNG uses uncertainty estimation via a VAE (variational autoencoder, a type of neural network that learns data patterns) to guide both exploration (trying new actions) and exploitation (using known good actions), achieving strong performance on standard benchmarks.