Lifelong RLExpert
How to Find and Reuse Policies for Continuous Adaptation in Lifelong Reinforcement Learning
In this preprint, researchers introduce Adaptive Mask Selection and Composition (AMSC), a lifelong reinforcement learning method that uses online task-similarity embeddings to choose and weight previously learned policy masks when adapting to new tasks. On CT28 and MiniGrid benchmarks, it reaches AUC/forward transfer of 0.94/0.94 and 0.73/0.54 with zero measured forgetting, exceeding modular baselines. The approach matters because it enables selective policy reuse instead of reusing an entire history, reducing interference and helping agents accumulate skills without destructive forgetting.