TSN-Affinity: Similarity-Driven Parameter Reuse for Continual Offline Reinforcement Learning

arXiv:2604.25898v1 Announce Type: cross Abstract: Continual offline reinforcement learning (CORL) aims to learn a sequence of tasks from datasets collected over time while preserving performance on previously learned tasks. This setting corresponds to domains where new tasks arise over time, but adapting the model in live environment interactions is expensive, risky, or impossible. However, CORL inherits the dual difficulty of offline reinforcement learning and adapting while preventing catastro