Abstract
We investigate opportunities and challenges for improving unsupervised
machine learning using four common strategies with a long history in physics:
divide-and-conquer, Occam's Razor, unification, and lifelong learning. Instead
of using one model to learn everything, we propose a novel paradigm centered
around the learning and manipulation of *theories*, which parsimoniously
predict both aspects of the future (from past observations) and the domain in
which these predictions are accurate. Specifically, we propose a novel
generalized-mean-loss to encourage each theory to specialize in its
comparatively advantageous domain, and a differentiable description length
objective to downweight bad data and "snap" learned theories into simple
symbolic formulas. Theories are stored in a "theory hub", which continuously
unifies learned theories and can propose theories when encountering new
environments. We test our implementation, the ÄI Physicist" learning agent, on
a suite of increasingly complex physics environments. From unsupervised
observation of trajectories through worlds involving random combinations of
gravity, electromagnetism, harmonic motion and elastic bounces, our agent
typically learns faster and produces mean-squared prediction errors about a
billion times smaller than a standard feedforward neural net of comparable
complexity, typically recovering integer and rational theory parameters
exactly. Our agent successfully identifies domains with different laws of
motion also for a nonlinear chaotic double pendulum in a piecewise constant
force field.
Description
1810.10525.pdf
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