The Need for a Big World Simulator: A Scientific Challenge for Continual Learning

Autor:	Kumar, Saurabh, Jeon, Hong Jun, Lewandowski, Alex, Van Roy, Benjamin
Rok vydání:	2024
Předmět:	Computer Science - Machine Learning Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	The "small agent, big world" frame offers a conceptual view that motivates the need for continual learning. The idea is that a small agent operating in a much bigger world cannot store all information that the world has to offer. To perform well, the agent must be carefully designed to ingest, retain, and eject the right information. To enable the development of performant continual learning agents, a number of synthetic environments have been proposed. However, these benchmarks suffer from limitations, including unnatural distribution shifts and a lack of fidelity to the "small agent, big world" framing. This paper aims to formalize two desiderata for the design of future simulated environments. These two criteria aim to reflect the objectives and complexity of continual learning in practical settings while enabling rapid prototyping of algorithms on a smaller scale. Comment: Accepted to the Finding the Frame Workshop at RLC 2024
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2408.02930 Zobrazit plný text záznamu View this record from Arxiv