Lifelong Machine Learning of Functionally Compositional Structures

TL;DR

This paper presents a unified framework for lifelong learning of compositional structures, enabling AI systems to learn, adapt, and reuse knowledge efficiently across diverse tasks in supervised and reinforcement learning.

Contribution

It introduces a two-stage framework that separates learning to combine components from adapting components, advancing lifelong compositional learning in AI.

Findings

Compositional models enhance lifelong learning of diverse tasks.

The framework's multi-stage process enables continual acquisition of compositional knowledge.

Modularity allows RL algorithms to adapt to nonstationary environments.

Abstract

A hallmark of human intelligence is the ability to construct self-contained chunks of knowledge and reuse them in novel combinations for solving different problems. Learning such compositional structures has been a challenge for artificial systems, due to the underlying combinatorial search. To date, research into compositional learning has largely proceeded separately from work on lifelong or continual learning. This dissertation integrated these two lines of work to present a general-purpose framework for lifelong learning of functionally compositional structures. The framework separates the learning into two stages: learning how to combine existing components to assimilate a novel problem, and learning how to adapt the existing components to accommodate the new problem. This separation explicitly handles the trade-off between stability and flexibility. This dissertation instantiated the framework into various supervised and reinforcement learning (RL) algorithms. Supervised learning evaluations found that 1) compositional models improve lifelong learning of diverse tasks, 2) the multi-stage process permits lifelong learning of compositional knowledge, and 3) the components learned by the framework represent self-contained and reusable functions. Similar RL evaluations demonstrated that 1) algorithms under the framework accelerate the discovery of high-performing policies, and 2) these algorithms retain or improve performance on previously learned tasks. The dissertation extended one lifelong compositional RL algorithm to the nonstationary setting, where the task distribution varies over time, and found that modularity permits individually tracking changes to different elements in the environment. The final contribution of this dissertation was a new benchmark for compositional RL, which exposed that existing methods struggle to discover the compositional properties of the environment.