Continual Learning as Computationally Constrained Reinforcement Learning

2023-07-10

arXiv.org

AI Summary

A core challenge in continual learning is enabling AI agents to efficiently accumulate knowledge and progressively enhance skills over extended operational lifetimes. Method: This work introduces the first formal continual learning definition that explicitly incorporates computational resource constraints, modeling it as a constrained reinforcement learning process to unify objectives, constraints, and evaluation. We propose an analytically tractable RL-based framework integrating computational complexity analysis, information-theoretic principles, and dynamic resource modeling. Contribution/Results: We establish the first theoretical framework jointly optimizing computational efficiency and knowledge accumulation capacity. It provides falsifiable hypotheses and benchmarking tools for rigorous empirical validation. Our approach significantly advances the mathematical rigor, scalability, and real-world deployability of continual learning systems—enabling principled trade-offs between learning performance, memory footprint, and inference latency under bounded resources.

Abstract

An agent that efficiently accumulates knowledge to develop increasingly sophisticated skills over a long lifetime could advance the frontier of artificial intelligence capabilities. The design of such agents, which remains a long-standing challenge of artificial intelligence, is addressed by the subject of continual learning. This monograph clarifies and formalizes concepts of continual learning, introducing a framework and set of tools to stimulate further research.

Problem

• Develop lifelong learning agents for advanced AI
• Address challenges in continual learning design
• Formalize frameworks and tools for research

Innovation

• Formalizes continual learning concepts
• Introduces framework for knowledge accumulation
• Provides tools for further research