We create a novel benchmark for evaluating a Deployable Lifelong Learning system for Visual Reinforcement Learning (RL) that is pretrained on a curated dataset, and propose a novel Scalable Lifelong Learning system capable of retaining knowledge from the previously learnt RL tasks. Our benchmark measures the efficacy of a deployable Lifelong Learning system that is evaluated on scalability, performance and resource utilization. Our proposed system, once pretrained on the dataset, can be deployed to perform continual learning on unseen tasks. Our proposed method consists of a Few Shot Class Incremental Learning (FSCIL) based task-mapper and an encoder/backbone trained entirely using the pretrain dataset. The policy parameters corresponding to the recognized task are then loaded to perform the task. We show that this system can be scaled to incorporate a large number of tasks due to the small memory footprint and fewer computational resources. We perform experiments on our DeLL (Deployment for Lifelong Learning) benchmark on the Atari games to determine the efficacy of the system.

翻译：我们构建了一个新颖的基准测试，用于评估在精选数据集上预训练的、面向视觉强化学习的可部署终身学习系统，并提出了一种新型可扩展终身学习系统，该系统能够保留先前所学强化学习任务的知识。该基准测试衡量可部署终身学习系统在可扩展性、性能和资源利用率方面的效能。我们提出的系统在完成数据集预训练后，可部署用于对未见任务进行持续学习。该方法包含一个基于小样本类增量学习的任务映射器，以及一个完全使用预训练数据集训练的编码器/骨干网络。随后加载与识别任务对应的策略参数以执行该任务。实验表明，由于内存占用小且计算资源需求低，该系统可扩展至包含大量任务。我们在Atari游戏上基于所提出的DeLL（终身学习部署）基准测试进行了实验，以验证系统的有效性。