In a lifelong learning framework, an agent acquires knowledge incrementally over consecutive learning tasks, continually building upon its experience. Recent lifelong learning algorithms have achieved nearly identical performance to batch multi-task learning methods while reducing learning time by three orders of magnitude. In this paper, we further improve the scalability of lifelong learning by developing curriculum selection methods that enable an agent to actively select the next task to learn in order to maximize performance on future learning tasks. We demonstrate that active task selection is highly reliable and effective, allowing an agent to learn high performance models using up to 50% fewer tasks than when the agent has no control over the task order. We also explore a variant of transfer learning in the lifelong learning setting in which the agent can focus knowledge acquisition toward a particular target task.

The recently developed Efficient Lifelong Learning Algorithm (ELLA) acquires knowledge incrementally over a sequence of tasks, learning a repository of latent model components that are sparsely shared between models. ELLA shows strong performance in comparison to other multi-task learning algorithms, achieving nearly identical performance to batch multi-task learning methods while learning tasks sequentially in three orders of magnitude (over 1,000x) less time. In this paper, we evaluate several curriculum selection methods that allow ELLA to actively select the next task for learning in order to maximize performance on future learning tasks. Through experiments with three real and one synthetic data set, we demonstrate that active curriculum selection allows an agent to learn up to 50% more efficiently than when the agent has no control over the task order.

The recently developed Efficient Lifelong Learning Algorithm (ELLA) acquires knowledge incrementally over a sequence of tasks, learning a repository of latent model components that are sparsely shared between models. ELLA shows strong performance in comparison to other multi-task learning algorithms, achieving nearly identical performance to batch multi-task learning methods while learning tasks sequentially in three orders of magnitude (over 1,000x) less time. In this paper, we evaluate several curriculum selection methods that allow ELLA to actively select the next task for learning in order to maximize performance on future learning tasks. Through experiments with three real and one synthetic data set, we demonstrate that active curriculum selection allows an agent to learn up to 50% more efficiently than when the agent has no control over the task order.