From Eye-blinks to State Construction: Diagnostic Benchmarks for Online Representation Learning

Abstract

Experiments in classical conditioning show that animals such as rabbits, pigeons, and dogs can make long temporal associations that enable multi-step prediction. To replicate this remarkable ability, an agent must construct an internal state representation that summarizes its interaction history. Recurrent neural networks can automatically construct state and learn temporal associations. But the current training methods are prohibitively expensive for online prediction -- continual learning on every time step -- which is the focus of this paper. To facilitate research in online prediction, we present three new diagnostic prediction problems inspired by classical-conditioning experiments. The proposed problems test the learning capabilities that animals readily exhibit and highlight the current recurrent learning methods' limitations. While the proposed problems are nontrivial, they are still amenable to extensive testing and analysis in the small-compute regime, thereby enabling researchers to study issues in isolation carefully, ultimately accelerating progress towards scalable online representation learning methods.