Modular Continual Learning in a Unified Visual Environment

TL;DR

This paper introduces a modular reinforcement learning framework with a unified visual environment, demonstrating improved task learning efficiency and flexible task switching through specialized module architectures and a dynamic meta-controller.

Contribution

It presents a novel modular continual learning paradigm with a unified visual environment and a dynamic meta-controller for efficient task switching, inspired by human intelligence.

Findings

Module motifs with specific design principles outperform standard neural networks.

The approach requires fewer training examples and neurons for high performance.

Dynamic neural voting enhances task switching and learning efficiency.

Abstract

A core aspect of human intelligence is the ability to learn new tasks quickly and switch between them flexibly. Here, we describe a modular continual reinforcement learning paradigm inspired by these abilities. We first introduce a visual interaction environment that allows many types of tasks to be unified in a single framework. We then describe a reward map prediction scheme that learns new tasks robustly in the very large state and action spaces required by such an environment. We investigate how properties of module architecture influence efficiency of task learning, showing that a module motif incorporating specific design principles (e.g. early bottlenecks, low-order polynomial nonlinearities, and symmetry) significantly outperforms more standard neural network motifs, needing fewer training examples and fewer neurons to achieve high levels of performance. Finally, we present a meta-controller architecture for task switching based on a dynamic neural voting scheme, which allows new modules to use information learned from previously-seen tasks to substantially improve their own learning efficiency.