Architectural parameter reuse guided by task similarity is a memory-efficient alternative to replay-based continual learning in offline RL, enabling better multi-task performance without storing historical data.
This paper presents TSN-Affinity, a method for continual offline reinforcement learning that learns multiple tasks sequentially from pre-collected datasets without forgetting previous tasks.