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We create a novel benchmark for evaluating a Deployable Lifelong Learningsystem for Visual Reinforcement Learning (RL) that is pretrained on a curateddataset, and propose a novel Scalable Lifelong Learning system capable ofretaining knowledge from the previously learnt RL tasks. Our benchmark measuresthe efficacy of a deployable Lifelong Learning system that is evaluated onscalability, performance and resource utilization. Our proposed system, oncepretrained on the dataset, can be deployed to perform continual learning onunseen tasks. Our proposed method consists of a Few Shot Class IncrementalLearning (FSCIL) based task-mapper and an encoder/backbone trained entirelyusing the pretrain dataset. The policy parameters corresponding to therecognized task are then loaded to perform the task. We show that this systemcan be scaled to incorporate a large number of tasks due to the small memoryfootprint and fewer computational resources. We perform experiments on our DeLL(Deployment for Lifelong Learning) benchmark on the Atari games to determinethe efficacy of the system.

Evaluating Pretrained models for Deployable Lifelong Learning