世界更大！一个计算嵌入视角下的大世界假说
The World Is Bigger! A Computationally-Embedded Perspective on the Big World Hypothesis
另 2 家 · 查看机构详情
- University of Alberta（阿尔伯塔大学）
- Amii
- The Swiss AI Lab IDSIA, USI & SUPSI（瑞士AI实验室IDSIA、USI与SUPSI）
- Canada CIFAR AI Chair（加拿大CIFAR人工智能主席）
- Google DeepMind（谷歌DeepMind）
机构由 AI 辅助整理，请以论文原文为准。
浏览论文内容
中文总结 AI 辅助
本文提出了一种计算嵌入视角下的持续学习方法，通过交互性目标评估智能体在无限状态空间中的适应能力，发现深度线性网络在扩展能力时表现更优。
中文摘要 AI 辅助
持续学习常常受到
英文摘要
Continual learning is often motivated by the idea, known as the big world hypothesis, that "the world is bigger" than the agent. Recent problem formulations capture this idea by explicitly constraining an agent relative to the environment. These constraints lead to solutions in which the agent continually adapts to best use its limited capacity, rather than converging to a fixed solution. However, explicit constraints can be ad hoc, difficult to incorporate, and may limit the effectiveness of scaling up the agent's capacity. In this paper, we characterize a problem setting in which an agent, regardless of its capacity, is constrained by being embedded in the environment. In particular, we introduce a computationally-embedded perspective that represents an embedded agent as an automaton simulated within a universal (formal) computer. Such an automaton is always constrained; we prove that it is equivalent to an agent that interacts with a partially observable Markov decision process over a countably infinite state-space. We propose an objective for this setting, which we call interactivity, that measures an agent's ability to continually adapt its behaviour by learning new predictions. We then develop a model-based reinforcement learning algorithm for interactivity-seeking, and use it to construct a synthetic problem to evaluate continual learning capability. Our results show that deep nonlinear networks struggle to sustain interactivity, whereas deep linear networks sustain higher interactivity as capacity increases.
