Year: 2020
Type: preprint
Abstract: World models are self-supervised predictive models of how the world evolves. Humans learn world models by curiously exploring their environment, in the process acquiring compact abstractions of high bandwidth sensory inputs, the ability to plan across long temporal horizons, and an understanding of the behavioral patterns of other agents. In this work, we study how to design such a curiosity-driven Active World Model Learning (AWML) system. To do so, we construct a curious agent building world models while visually exploring a 3D physical environment rich with distillations of representative real-world agents. We propose an AWML system driven by $γ$-Progress: a scalable and effective learning progress-based curiosity signal. We show that $γ$-Progress naturally gives rise to an exploration policy that directs attention to complex but learnable dynamics in a balanced manner, thus overcoming the "white noise problem". As a result, our $γ$-Progress-driven controller achieves significantly higher AWML performance than baseline controllers equipped with state-of-the-art exploration strategies such as Random Network Distillation and Model Disagreement.
Source: arXiv (Cornell University)
Institutions: Stanford University, Harvard University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Awards: G2004185613, CAREER: Understanding visual learning with self-supervised neural network models, G6671297155, RI: Medium: Collaborative Research: Incorporating Biological-Motivated Circuit Motifs into Large-Scale Deep Neural Network Models of the Brain, Building richer computational models of visual cognition
OpenAlex ID: W3042772609